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Research ArticleA Vehicle Detection Algorithm Based on Deep Belief Network
Hai Wang1 Yingfeng Cai2 and Long Chen2
1 School of Automotive and Traffic Engineering Jiangsu University Zhenjiang 212013 China2 Automotive Engineering Research Institute Jiangsu University Zhenjiang 212013 China
Correspondence should be addressed to Yingfeng Cai caicaixiao0304126com
Received 25 March 2014 Accepted 22 April 2014 Published 15 May 2014
Academic Editor Yu-Bo Yuan
Copyright copy 2014 Hai Wang et alThis is an open access article distributed under theCreativeCommonsAttributionLicensewhichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also roadvideo surveillance application Traditional shallow model based vehicle detection algorithm still cannot meet the requirement ofaccurate vehicle detection in these applications In this work a novel deep learning based vehicle detection algorithm with 2Ddeep belief network (2D-DBN) is proposed In the algorithm the proposed 2D-DBN architecture uses second-order planes insteadof first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size ofthe deep architecture which enhances the success rate of vehicle detection On-road experimental results demonstrate that thealgorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets
1 Introduction
Robust vision based vehicle detection on the road is tosome extent a challenging problem since highways and urbanand city roads are dynamic environment in which thebackground and illuminations are dynamic and time variantBesides the shape color size and appearance of vehicles areof high variability To make this task even more difficult theego vehicle and target vehicles are generally in motion so thatthe size and location of target vehicles mapped to the imageare diverse
Although deep learning for object recognition has beenan area of great interest in the machine-learning communityno prior research study has been reported that uses deeplearning to establish an on-road vehicle detection methodIn this paper a 2D-DBN based vehicle detection algorithmis proposed
The main novelty and contribution of this work includethe following (1) A deep learning architecture of 2D-DBN which preserves discriminative information for vehicledetection is proposed (2) A deep learning based on-roadvehicle detection systemhas been implemented and thoroughquantitative performance analysis has been presented
The rest of this paper is organized as follows Section 2will give a brief talking about vision based vehicle detection
tasks and deep learning for object recognition Section 3introduces in detail the proposed 2D-DBN architecture andtraining methods for the vehicle detection tasks The experi-ments and analysis will be given in Section 4 and Section 5 isthe conclusion
2 Related Research
In this section a brief overview of two categories of work thatis relevant to our research is introduced The first set is aboutvision based vehicle detection and the second focuses on deeplearning for object recognition
21 Vision Based Vehicle Detection Since only monocularvisual perception is used in our project this section willmainly refer to studies using monocular vision for on-roadvehicle detection
For monocular vision based vehicle detection usingvehicle appearance characteristics is the most common andeffective approach A variety of appearance features have beenused in the field to detect vehicles Some typical image fea-tures representing intuitive vehicle appearance informationsuch as local symmetry edge and cast shadow have beenused by many earlier works
Hindawi Publishing Corporatione Scientific World JournalVolume 2014 Article ID 647380 7 pageshttpdxdoiorg1011552014647380
2 The Scientific World Journal
Hidden layer
Hidden layer
Hidden layer
Input layer
RBM
RBM
RBM
x
y
H1
H2
H1
V1 V1V1
HN
HNminus1
H1
Figure 1 Architecture of deep belief network (DBN)
In recent years there has been a transition from sim-pler image features to general and robust feature sets forvehicle detection These feature sets now common in thecomputer vision literature allow for direct classification anddetection of objects in images For vehicle detection purposehistogram of oriented gradient (HOG) features and Haar-like features are extremely well represented in literatureBesides Gabor features scale-invariant feature transform(SIFT) features speeded up robust features (SURF) andsome combined features are also applied for vehicle imagerepresentation
Classification methods for appearance-based vehicledetection have also followed the general trends in thecomputer vision and machine learning literature Comparedto generative classifiers discriminative classifiers whichlearn a decision boundary between two classes (vehiclesand unvehicles) have been more widely used in vehicledetection application Support vector machines (SVM) andAdaboost are the most two common classifiers that are usedfor training vehicle detector In [1] SVM classification wasused to classify Haar feature vectors The combination ofHOG features and SVM classification has also been used[2ndash4] Adaboost [5] has also been widely used for classifi-cation for Viola and Jonesrsquo contribution The combinationof Haar-like feature extraction and Adaboost classificationhas been used to detect rear faces of vehicles in [6ndash9]Artificial neural network classifiers were also used for vehi-cle detection but the training often failed due to localoptimum [10]
22 Deep Learning for Object Recognition Classifiers such asSVM and Adaboost referred to in last section are all indeed ashallow learning model because they both can be modeled asstructure with one input layer one hidden layer and one out-put layer Deep learning refers to a class of machine learningtechniques where hierarchical architectures are exploited forrepresentation learning and pattern classification Differentfrom those shallow models deep learning has the ability oflearningmultiple levels of representation and abstraction thathelps to make sense of image data From another point ofview deep learning can be viewed as one kind of multilayer
neural networks with adding a novel unsurprised pretrainingprocess
There are various subclasses of deep architecture Deepbelief networks (DBN) modal is a typical deep learningstructure which is first proposed by Hinton et al [11] Theoriginal DBN has demonstrated its success in simple imageclassification tasks of MNIST In [12] a modified DBN isdeveloped in which a Boltzmann machine is used on the toplayer This modified DBN is used in a 3D object recognitiontask
Deep convolutional neural network (DCNN) with theability to preserve the space structure and resistance to smallvariations in the images is used in image classification [13]Recently DCNN achieved the best performance compared toother state-of-the-art methods in the 2012 ImageNet LSVRCcontest containing 12 million images with more than 1000different classes In this DCNN application a very largearchitecture is built withmore than 600000 neurons and over60 million weights
DBN is a probabilistic model composed ofmultiple layersof stochastic hidden variables The learning procedure ofDBN can be divided into two stages generative learning toabstract information layer by layer with unlabelled samplesfirstly and then discriminative learning to fine-tune thewholedeep network with labeled samples to the ultimate learningtarget [11] Figure 1 shows a typical DBN with one input layer1198811and N hidden layers 119867
1 1198672 119867
119873 while x is the input
data which can be for example a vector and y is the learningtarget for example class labels In the unsupervised stage ofDBN training processes each pair of layers grouped togetherto reconstruct the input of the layer from the output InFigure 1 the layer-wise reconstruction happens between 119881
1
and 1198671 1198671and 119867
2 119867
119873minus1and 119867
119899 respectively which is
implemented by a family of restricted Boltzmann machines(RBMs) [14] After the greedy unsupervised learning of eachpair of layers the features are progressively combined fromloose low-level representations into more compact high-level representations In the supervised stage the wholedeep network is then refined using a contrastive versionof the ldquowake-sleeprdquo algorithm via a global gradient-basedoptimization strategy
The Scientific World Journal 3
3 Deep Learning Based Vehicle Detection
In this section a novel algorithm based on deep beliefnetwork (DBN) is proposed Traditional DBN for objectclassification has some shortages First the training samplesare regularized to first-order vector which will lead tothe missing of spatial information contained by the imagesamples This will obviously lead to a decline in the detectionrate in vehicle detection tasks Secondly the size of layers(such as node number and layer number) in the traditionalDBN is manually set which is often big and will lead tostructural redundancy and increase the training and decisiontime of the classifier while for vehicle detection algorithmwhich is usually used in real-time application decision timeis a critical factor The proposed 2D-DBN architecture forvehicle detection uses second-order planes instead of first-order vector of 1D-DBN as input and uses bilinear projectionfor retaining discriminative information so as to determinethe size of the deep architectureThe bilinear projectionmapsoriginal second-order output of lower layer to a small bilinearspace without reducing discriminative information And thesize of the upper layer is that of the bilinear space
In Section 31 the overall architecture of our 2D-DBNfor vehicle detection will be introduced In Section 32 thebilinear projectionmethod of lower layer outputwill be givenIn Sections 33 and 34 the training method of the whole 2D-DBN for vehicle detection will be deduced
31 2119863 Deep Belief Network (2D-DBN) for Vehicle DetectionLet 119883 be the set of data samples including vehicle imagesand nonvehicle images assuming that 119883 is consisting withK samples which is shown below
119883 = [X1X2 X
119896 X
119870] (1)
In 119883 X119896is training samples and in the image space R119868times119869
Meanwhile 119884 means the labels corresponding to 119883 whichcan be written as
119884 = [1199101 1199102 119910
119896 119910
119870] (2)
In 119884 119910119896is the label vector of X
119896 If X119896belongs to vehicles
119910119896= (1 0) On the contrary 119910
119896= (0 1)
The ultimate purpose in vehicle detection task is to learna mapping function from training data 119883 to the label data119884 based on the given training set so that this mappingfunction is able to classify unknown images between vehicleand nonvehicle
Based on the task described above a novel 2D deep beliefnetwork (2D-DBN) is proposed to address this problemFigure 2 shows the overall architecture of 2D-DBN A fullyinterconnected directed belief network includes one visibleinput layer 1198811 119873 hidden layers 1198671 119867119873 and one visiblelabel layer La at the top The visible input layer 1198811 maintains119872times119873 neural and equal to the dimension of training featurewhich is the original 2D image pixel values of training sam-ples in this application Sincemaximumdiscriminative abilitywishes to be preserved from layer to layer with nonredundantlayer size in this application for real-time requirement the
y1
ky2
k
La
HN
HNminus1
H1
V1
Xk
Visible layer
Feature input
Label layer
Hidden layer
Figure 2 Proposed 2D-DBN for vehicle detection
size of119873 hidden layers is dynamically decided with so-calledbilinear projection In the top the La layer just has two unitswhich is equal to the classes this application would like toclassify Till now the problem is formulated to search for theoptimum parameter space 120579 of this 2D-DBN
The main learning process of the proposed 2D-DBN hasthree steps
(1) The bilinear projection is utilized to map the lowerlayer output data onto subspace and optimized tooptimum dimension as well as to retain discrimina-tive information The size of the upper layer will bedetermined by this optimum dimension
(2) When the size of the upper layer is determined theparameters of the two adjacent layers will be refinedwith the greedy-wise reconstruction method Repeatstep (1) and step (2) till all the parameters of hiddenlayers are fixedHere step (1) and step (2) are so calledpretraining process
(3) Finally the whole 2D-DBN will be fine-tuned withthe La layer information based on back propagationHere step (3) can be viewed as supervised trainingstep
32 Bilinear Projection for Upper Layer Size DeterminationIn this section followed with Zhongrsquos contribution [15]bilinear projection is used in order to determine the size ofevery upper layer in adjacent layer groups As mentioned inSection 31 with the labeled training data X
119896isin R119868times119869 as the
output of the visible layer 1198811 bilinear projection maps the
4 The Scientific World Journal
original data X119896onto a subspace and is represented by its
latent form LX119896 The bilinear projection is written as follows
LX119896= U119879X
119896V 119896 = 1 2 119870 (3)
Here U isin 119877119872times119875 and V isin 119877119873times119876 are projection matricesthat map the original data X
119896by its latent form LX
119896with the
constraint that U119879U = I and V119879V = IHow to determine the value of U and V so that the
discriminative information ofX119896can be preserved is the issue
that needs to be solved For this a specific objective functionis built as follows
argmaxUV
119869 (UV) = sum119894119895
10038171003817100381710038171003817U119879 (X
119894minus X119895)V10038171003817100381710038171003817
2
times (120572119861119894119895minus (1 minus 120572)119882
119894119895)
(4)
in which 119861119894119895is between-class weights 119882
119894119895is within class
weights and 120572 isin [0 1] is the balance parameter 119861119894119895and 119882
119894119895
are calculated as follows [16]
119861119894119895=
minus119899119899119888
(119899119899119888
+ 119899119888) 119899119888
if 119910119888119894= 119910119888119895= 1
1
119899119899119888
+ 119899119888
else
119882119894119895=
1
119899119888
if 119910119888119894= 119910119888119895= 1
0 else
(5)
Here 119910119888119894is the class label of sample data X
119894 which is either
(1 0) or (0 1) 119899119888is the number of samples that belong to
class 119888 and 119899119888is those not belonging to class 119888 Since vehicle
detection is a binary classification problem 119888 isin 1 2 in thisapplication
It can be seen that the purpose of the objective functionis to simultaneously maximize the between-class distancesand minimize the within-class distances In other words theobjective function focuses on maximizing the discriminativeinformation of all the sample data However optimizing119869(UV) is a nonconvex optimization problem with twomatrices U and V To deal with this trouble a strategy calledalternative fixing (AF) is used which is fixing U (or V) andoptimizing the objective function 119869(UV) with just variablematrix V (or U) and then fixing V (or U) and optimizing119869(UV) with just U (or V) AF will be implemented alterna-tively till 119869(UV) reaches its upper bound
After the optimum process new Ulowast and Vlowast that maxi-mize 119869(UV) are got and preserve the discriminative informa-tion of original sample data119883 Based on this then the size ofthe upper layer can be determined by the number of positiveeigenvalues of Ulowast and Vlowast which is 119875 and 119876 respectively
33 Pretraining with Greedy Layer-Wise ReconstructionMethod In last subsection the size of the upper layer isdetermined to be 119875times119876 In this subsection the parameters ofthe two adjacent layers will be refined with the greedy-wisereconstruction method proposed by Hinton et al [11] To
illustrate this pretraining process we take the visible inputlayer 1198811 and the first hidden layer 1198671 for example
The visible input layer 1198811 and the first hidden layer 1198671
contract a restrict Boltzmann machine (RBM) 119868 times 119869 is theneural number in 1198811 and 119875 times 119876 is that of 1198671 The energy ofthe state (V1ℎ1) in this RBM is
119864 (k1 h1 1205791) = minus (k1Ah1 + b1k1 + c1h1)
= minus
119894le119868119895le119869
sum119894=1119895=1
119901le119875119902le119876
sum119901=1119902=1
V11198941198951198601
119894119895119901119902ℎ1
119901119902
minus
119894le119868119895le119869
sum119894=1119895=1
1198871
119894119895V1119894119895minus
119901le119875119902le119876
sum119901=1119902=1
1198881
119901119902ℎ1
119901119902
(6)
in which 1205791 = (A1 b1 c1) are the parameters between thevisible input layer 1198811 and the first hidden layer 1198671 1198601
119894119895119901119902
is the symmetric weights from input neural (119894 119895) in 1198811 tothe hidden neural (119901 119902) in 119867
1 1198871119894119895and 1198881
119901119902are the (119894 119895)
119905ℎ and(119901 119902)119905ℎ bias of 1198811 and 1198671 So this RBM is with the joint
distribution as follows
119875 (k1 h1 1205791) =1
119885119890minus119864(k1 h11205791)
=119890minus119864(k
1h11205791)
sumV1 sumℎ1 119890minus119864(k1 h11205791)
(7)
Here Z is the normalization parameter and the probabilitythat k1 is assigned to 1198811 of this modal is
119875 (k1) =1
119885sum
ℎ1
119890minus119864(k1 h1 1205791)
=sumℎ1 119890minus119864(k1 h11205791)
sumV1 sumℎ1 119890minus119864(k1 h11205791) (8)
After that the conditional distributions over visible inputstate k1 in layer 1198811 and hidden state ℎ1 in 1198671 are able to begiven by the logistic function respectively
119901 (h1 | k1) = prod119901119902
119901 (ℎ1
119901119902| k1) 119901 (ℎ
1
119901119902| k1)
= 120590(
119894le119868119895le119869
sum119894=1119895=1
V11198941198951198601
119894119895119901119902+ 1198881
119901119902)
(9)
119901 (k1 | h1) = prod119894119895
119901 (k1119894119895| h1) 119901 (k1
119894119895| h1)
= 120590(
119901le119875119902le119876
sum119901=1119902=1
ℎ1
1199011199021198601
119894119895119901119902+ 1198871
119894119895)
(10)
Here 120590(119909) = 1(1 + exp(minus119909))At last the weights and biases are able to be updated
step by step from random Gaussian distribution values
The Scientific World Journal 5
(a) (b)
Figure 3 Some positive and negative training samples (a) Positive samples (b) Negative samples
1198601119894119895119901119902
(0) 1198871119894119895(0) and 1198881
119901119902(0)with Contrastive Divergence algo-
rithm [17] and the updating formulations are
1198601
119894119895119901119902= 1205991198601
119894119895119901119902
+ 120576119860(⟨V1119894119895(0) ℎ1
119894119895(0)⟩
dataminus ⟨V1119894119895(119905) ℎ1
119894119895(119905)⟩
recon)
1198871
119894119895= 1205991198871
119894119895+ 120576119887(V1119894119895(0) minus V1
119894119895(119905))
1198881
119901119902= 1205991198881
119901119902+ 120576119888(ℎ1
119901119902(0) minus ℎ
1
119901119902(119905))
(11)
in which ⟨sdot⟩data means the expectation with respect tothe data distribution and ⟨sdot⟩recon means the reconstructiondistribution after one step Meanwhile 119905 is step size which isset to 119905 = 1 typically
Above the pretraining process is demonstrated by takingthe visible input layer 119881
1 and the first hidden layer 1198671
for example Indeed the whole pretraining process will betaken from low layer groups (1198811 1198671) to up layer groups(119867119899minus1 119867119899) one by one
34 Global Fine-Tuning In the above unsurprised pretrain-ing process the greedy layer-wise algorithm is used tolearn the 2D-DBN parameters with the information addedfrom bilinear projection In this subsection a traditionalback propagation algorithm will be used to fine-tune theparameters 120579 = [A b c] with the information of label layerLa
Since good parameters initiation has been maintained inthe pretraining process back propagation is just utilized tofinely adjust the parameters so that local optimumparameters120579lowast = [Alowast blowast clowast] can be got In this stage the learning objec-tion is to minimize the classification error [minussum
119905y119905log y119905]
where y119905and y119905are the real label and output label of data X
119905
in layer 119873
4 Experiment and Analysis
This section will take experiments on vehicle datasets todemonstrate the performance of the proposed 2D-DBNThedatasets for training are Caltech1999 database which includesimages containing 126 rear view vehicles Besides another
Table 1 Detection results of three different architectures of 2D-DBN
Classifier types Correct labeling Correct rate2D-DBN (1H) 689735 93742D-DBN (2H) 706735 96052D-DBN (3H) 695735 9456
600 vehicles in images are collected by our groups in recordedroad videos for training Meanwhile the negative samplesare chosen from 500 images not containing vehicles and thenumber of negative samples for training is 5000 Figure 3shows some of these positive and negative training samplesThe testing datasets are recorded road videoswith 735manualmarked vehicles
By using the proposed method three different architec-tures of 2D-DBN are applied They all contain one visiblelayer and one label layer but with one two and three hiddenlayers respectively In training the critical parameters of theproposed 2D-DBN in experiments are set as 120572 = 05 and120599 = 08 and image samples for training are all resized to32 times 32
The detection results of these three architectures of 2D-DBN are shown in Table 1 It can be seen that 2D-DBN withtwo hidden layers maintains the highest detection rate
The learned weights of hidden layers are shownin Figure 4
Then we compared the performance of our 2D-DBNwithmany other state-of-the-art classifiers including supportvector machine (SVM) k-nearest neighbor (KNN) neuralnetworks 1D-DBN and deep convoluted neural network(DCNN)
The detection results of these methods are shown inTable 2
From the compared results it can be concluded thatclassification methods with deep architecture for example1D-DBN DCNN and 2D-DBN are significantly better thanthose of shallow architecture for example SVM KNN andNN Moreover our proposed 2D-DBN is better than 1D-DBN and DCNN due to 2D feature input and the bilinearprojection
Finally this 2D-DBN vehicle detectionmethod is utilizedon road vehicle detection system and some of the vehicle
6 The Scientific World Journal
Learned weights on DBN layer 1
(a)
Learned weights on DBN layer 2
(b)
Figure 4 Learned weights of first hidden layer and second hidden layer on 2D-DBN (a) weights of first hidden layer and (b) weights ofsecond hidden layer
Table 2 Detection results of multiple methods
Classifier types Correct labeling Correct rateSVM 658735 8952KNN 642735 8735NN 619735 84211D-DBN 684735 9306DCNN 697735 94832D-DBN (2H) 706735 9605
sensing results in real road situation are shown in Figure 5The four rows of images are picked in daylight highwayraining day highway daylight urban and night highway withroad lamp respectively The solid green box means detectedvehicles and the dotted red boxmeans undetected vehicles orfalse detected vehiclesThe average vehicle detection time forone frame with 640 times 480 resolution is around 53ms in ourAdvantech industrial computer
Overall most of the on-road vehicles can be sensed suc-cessfully while misdetection and false detection sometimesoccurred during adverse situations such as partial occlusionand bad weather
5 Conclusion
In thiswork a novel vehicle detection algorithmbased on 2D-DBN is proposed In the algorithm the proposed 2D-DBNarchitecture uses second-order planes instead of first-ordervector as input and uses bilinear projection for retainingdiscriminative information so as to determine the size of thedeep architecture which enhances the success rate of vehicledetectionOn-road experimental results demonstrate that thesystem works well in different roads weather and lightingconditions
The future work of our researchwill focus on the situationwhen a vehicle is partially occluded with deep architectureframework
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Figure 5 Some of the real road vehicle sensing results Firstrow daylight highway situation second row raining day highwaysituation third row daylight urban situation forth row nighthighway with road lamp
Acknowledgments
This research was funded partly and was supported inpart by the National Natural Science Foundation of Chinaunder Grant no 51305167 Information Technology ResearchProgram of Transport Ministry of China under Grant no2013364836900 and Jiangsu University Scientific ResearchFoundation for Senior Professionals under Grant no12JDG010
References
[1] W Liu X Wen B Duan H Yuan and N Wang ldquoRear vehicledetection and tracking for lane change assistrdquo in Proceedings ofthe IEEE Intelligent Vehicles Symposium (IV rsquo07) pp 252ndash257June 2007
[2] R Miller Z Sun and G Bebis ldquoMonocular precrash vehicledetection features and classifiersrdquo IEEE Transactions on ImageProcessing vol 15 no 7 pp 2019ndash2034 2006
The Scientific World Journal 7
[3] S Sivaraman and M M Trivedi ldquoActive learning for on-roadvehicle detection a comparative studyrdquo Machine Vision andApplications pp 1ndash13 2011
[4] S Teoh and T Brunl ldquoSymmetry-based monocular vehicledetection systemrdquoMachine Vision and Applications vol 23 pp831ndash842 2012
[5] R Sindoori K Ravichandran and B Santhi ldquoAdaboost tech-nique for vehicle detection in aerial surveillancerdquo InternationalJournal of Engineering amp Technology vol 5 no 2 2013
[6] J Cui F Liu Z Li and Z Jia ldquoVehicle localisation using asingle camerardquo in Proceedings of the IEEE Intelligent VehiclesSymposium (IV rsquo10) pp 871ndash876 June 2010
[7] T T Son and S Mita ldquoCar detection using multi-featureselection for varying posesrdquo inProceedings of the IEEE IntelligentVehicles Symposium pp 507ndash512 June 2009
[8] D Acunzo Y Zhu B Xie and G Baratoff ldquoContext-adaptiveapproach for vehicle detection under varying lighting condi-tionsrdquo in Proceedings of the 10th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo07) pp 654ndash660October 2007
[9] C T Lin S C Hsu J F Lee et al ldquoBoosted vehicle detectionusing local and global featuresrdquo Journal of Signal amp InformationProcessing vol 4 no 3 2013
[10] O Ludwig Jr and U Nunes ldquoImproving the generalizationproperties of neural networks an application to vehicle detec-tionrdquo in Proceedings of the 11th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo08) pp 310ndash315December 2008
[11] G E Hinton S Osindero and Y-W Teh ldquoA fast learningalgorithm for deep belief netsrdquoNeural Computation vol 18 no7 pp 1527ndash1554 2006
[12] V Nair and G E Hinton ldquo3D object recognition with deepbelief netsrdquo in Proceedings of the 23rd Annual Conference onNeural Information Processing Systems (NIPS rsquo09) pp 1339ndash1347December 2009
[13] G Taylor R Fergus Y L Cun and C Bregler ldquoConvolutionallearning of spatio-temporal featuresrdquo in Computer VisionmdashECCV 2010 vol 6316 of Lecture Notes in Computer Science pp140ndash153 2010
[14] C X Zhang J S Zhang N N Ji et al ldquoLearning ensemble clas-sifiers via restricted Boltzmann machinesrdquo Pattern RecognitionLetters vol 36 pp 161ndash170 2014
[15] S-H Zhong Y Liu and Y Liu ldquoBilinear deep learning forimage classificationrdquo in Proceedings of the 19th ACM Interna-tional Conference on Multimedia ACM Multimedia (SIG MMrsquo11) December 2011
[16] S YanD Xu B ZhangH-J ZhangQ Yang and S Lin ldquoGraphembedding and extensions a general framework for dimen-sionality reductionrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 29 no 1 pp 40ndash51 2007
[17] F Wood and G E Hinton ldquoTraining products of experts byminimizing contrastive divergencerdquo Tech Rep 2012 53 143Brown University 2012
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![Page 2: Research Article A Vehicle Detection Algorithm Based on ...downloads.hindawi.com/journals/tswj/2014/647380.pdf · Vision based vehicle detection is a critical technology that plays](https://reader035.vdocument.in/reader035/viewer/2022062918/5ededf25ad6a402d666a3a51/html5/thumbnails/2.jpg)
2 The Scientific World Journal
Hidden layer
Hidden layer
Hidden layer
Input layer
RBM
RBM
RBM
x
y
H1
H2
H1
V1 V1V1
HN
HNminus1
H1
Figure 1 Architecture of deep belief network (DBN)
In recent years there has been a transition from sim-pler image features to general and robust feature sets forvehicle detection These feature sets now common in thecomputer vision literature allow for direct classification anddetection of objects in images For vehicle detection purposehistogram of oriented gradient (HOG) features and Haar-like features are extremely well represented in literatureBesides Gabor features scale-invariant feature transform(SIFT) features speeded up robust features (SURF) andsome combined features are also applied for vehicle imagerepresentation
Classification methods for appearance-based vehicledetection have also followed the general trends in thecomputer vision and machine learning literature Comparedto generative classifiers discriminative classifiers whichlearn a decision boundary between two classes (vehiclesand unvehicles) have been more widely used in vehicledetection application Support vector machines (SVM) andAdaboost are the most two common classifiers that are usedfor training vehicle detector In [1] SVM classification wasused to classify Haar feature vectors The combination ofHOG features and SVM classification has also been used[2ndash4] Adaboost [5] has also been widely used for classifi-cation for Viola and Jonesrsquo contribution The combinationof Haar-like feature extraction and Adaboost classificationhas been used to detect rear faces of vehicles in [6ndash9]Artificial neural network classifiers were also used for vehi-cle detection but the training often failed due to localoptimum [10]
22 Deep Learning for Object Recognition Classifiers such asSVM and Adaboost referred to in last section are all indeed ashallow learning model because they both can be modeled asstructure with one input layer one hidden layer and one out-put layer Deep learning refers to a class of machine learningtechniques where hierarchical architectures are exploited forrepresentation learning and pattern classification Differentfrom those shallow models deep learning has the ability oflearningmultiple levels of representation and abstraction thathelps to make sense of image data From another point ofview deep learning can be viewed as one kind of multilayer
neural networks with adding a novel unsurprised pretrainingprocess
There are various subclasses of deep architecture Deepbelief networks (DBN) modal is a typical deep learningstructure which is first proposed by Hinton et al [11] Theoriginal DBN has demonstrated its success in simple imageclassification tasks of MNIST In [12] a modified DBN isdeveloped in which a Boltzmann machine is used on the toplayer This modified DBN is used in a 3D object recognitiontask
Deep convolutional neural network (DCNN) with theability to preserve the space structure and resistance to smallvariations in the images is used in image classification [13]Recently DCNN achieved the best performance compared toother state-of-the-art methods in the 2012 ImageNet LSVRCcontest containing 12 million images with more than 1000different classes In this DCNN application a very largearchitecture is built withmore than 600000 neurons and over60 million weights
DBN is a probabilistic model composed ofmultiple layersof stochastic hidden variables The learning procedure ofDBN can be divided into two stages generative learning toabstract information layer by layer with unlabelled samplesfirstly and then discriminative learning to fine-tune thewholedeep network with labeled samples to the ultimate learningtarget [11] Figure 1 shows a typical DBN with one input layer1198811and N hidden layers 119867
1 1198672 119867
119873 while x is the input
data which can be for example a vector and y is the learningtarget for example class labels In the unsupervised stage ofDBN training processes each pair of layers grouped togetherto reconstruct the input of the layer from the output InFigure 1 the layer-wise reconstruction happens between 119881
1
and 1198671 1198671and 119867
2 119867
119873minus1and 119867
119899 respectively which is
implemented by a family of restricted Boltzmann machines(RBMs) [14] After the greedy unsupervised learning of eachpair of layers the features are progressively combined fromloose low-level representations into more compact high-level representations In the supervised stage the wholedeep network is then refined using a contrastive versionof the ldquowake-sleeprdquo algorithm via a global gradient-basedoptimization strategy
The Scientific World Journal 3
3 Deep Learning Based Vehicle Detection
In this section a novel algorithm based on deep beliefnetwork (DBN) is proposed Traditional DBN for objectclassification has some shortages First the training samplesare regularized to first-order vector which will lead tothe missing of spatial information contained by the imagesamples This will obviously lead to a decline in the detectionrate in vehicle detection tasks Secondly the size of layers(such as node number and layer number) in the traditionalDBN is manually set which is often big and will lead tostructural redundancy and increase the training and decisiontime of the classifier while for vehicle detection algorithmwhich is usually used in real-time application decision timeis a critical factor The proposed 2D-DBN architecture forvehicle detection uses second-order planes instead of first-order vector of 1D-DBN as input and uses bilinear projectionfor retaining discriminative information so as to determinethe size of the deep architectureThe bilinear projectionmapsoriginal second-order output of lower layer to a small bilinearspace without reducing discriminative information And thesize of the upper layer is that of the bilinear space
In Section 31 the overall architecture of our 2D-DBNfor vehicle detection will be introduced In Section 32 thebilinear projectionmethod of lower layer outputwill be givenIn Sections 33 and 34 the training method of the whole 2D-DBN for vehicle detection will be deduced
31 2119863 Deep Belief Network (2D-DBN) for Vehicle DetectionLet 119883 be the set of data samples including vehicle imagesand nonvehicle images assuming that 119883 is consisting withK samples which is shown below
119883 = [X1X2 X
119896 X
119870] (1)
In 119883 X119896is training samples and in the image space R119868times119869
Meanwhile 119884 means the labels corresponding to 119883 whichcan be written as
119884 = [1199101 1199102 119910
119896 119910
119870] (2)
In 119884 119910119896is the label vector of X
119896 If X119896belongs to vehicles
119910119896= (1 0) On the contrary 119910
119896= (0 1)
The ultimate purpose in vehicle detection task is to learna mapping function from training data 119883 to the label data119884 based on the given training set so that this mappingfunction is able to classify unknown images between vehicleand nonvehicle
Based on the task described above a novel 2D deep beliefnetwork (2D-DBN) is proposed to address this problemFigure 2 shows the overall architecture of 2D-DBN A fullyinterconnected directed belief network includes one visibleinput layer 1198811 119873 hidden layers 1198671 119867119873 and one visiblelabel layer La at the top The visible input layer 1198811 maintains119872times119873 neural and equal to the dimension of training featurewhich is the original 2D image pixel values of training sam-ples in this application Sincemaximumdiscriminative abilitywishes to be preserved from layer to layer with nonredundantlayer size in this application for real-time requirement the
y1
ky2
k
La
HN
HNminus1
H1
V1
Xk
Visible layer
Feature input
Label layer
Hidden layer
Figure 2 Proposed 2D-DBN for vehicle detection
size of119873 hidden layers is dynamically decided with so-calledbilinear projection In the top the La layer just has two unitswhich is equal to the classes this application would like toclassify Till now the problem is formulated to search for theoptimum parameter space 120579 of this 2D-DBN
The main learning process of the proposed 2D-DBN hasthree steps
(1) The bilinear projection is utilized to map the lowerlayer output data onto subspace and optimized tooptimum dimension as well as to retain discrimina-tive information The size of the upper layer will bedetermined by this optimum dimension
(2) When the size of the upper layer is determined theparameters of the two adjacent layers will be refinedwith the greedy-wise reconstruction method Repeatstep (1) and step (2) till all the parameters of hiddenlayers are fixedHere step (1) and step (2) are so calledpretraining process
(3) Finally the whole 2D-DBN will be fine-tuned withthe La layer information based on back propagationHere step (3) can be viewed as supervised trainingstep
32 Bilinear Projection for Upper Layer Size DeterminationIn this section followed with Zhongrsquos contribution [15]bilinear projection is used in order to determine the size ofevery upper layer in adjacent layer groups As mentioned inSection 31 with the labeled training data X
119896isin R119868times119869 as the
output of the visible layer 1198811 bilinear projection maps the
4 The Scientific World Journal
original data X119896onto a subspace and is represented by its
latent form LX119896 The bilinear projection is written as follows
LX119896= U119879X
119896V 119896 = 1 2 119870 (3)
Here U isin 119877119872times119875 and V isin 119877119873times119876 are projection matricesthat map the original data X
119896by its latent form LX
119896with the
constraint that U119879U = I and V119879V = IHow to determine the value of U and V so that the
discriminative information ofX119896can be preserved is the issue
that needs to be solved For this a specific objective functionis built as follows
argmaxUV
119869 (UV) = sum119894119895
10038171003817100381710038171003817U119879 (X
119894minus X119895)V10038171003817100381710038171003817
2
times (120572119861119894119895minus (1 minus 120572)119882
119894119895)
(4)
in which 119861119894119895is between-class weights 119882
119894119895is within class
weights and 120572 isin [0 1] is the balance parameter 119861119894119895and 119882
119894119895
are calculated as follows [16]
119861119894119895=
minus119899119899119888
(119899119899119888
+ 119899119888) 119899119888
if 119910119888119894= 119910119888119895= 1
1
119899119899119888
+ 119899119888
else
119882119894119895=
1
119899119888
if 119910119888119894= 119910119888119895= 1
0 else
(5)
Here 119910119888119894is the class label of sample data X
119894 which is either
(1 0) or (0 1) 119899119888is the number of samples that belong to
class 119888 and 119899119888is those not belonging to class 119888 Since vehicle
detection is a binary classification problem 119888 isin 1 2 in thisapplication
It can be seen that the purpose of the objective functionis to simultaneously maximize the between-class distancesand minimize the within-class distances In other words theobjective function focuses on maximizing the discriminativeinformation of all the sample data However optimizing119869(UV) is a nonconvex optimization problem with twomatrices U and V To deal with this trouble a strategy calledalternative fixing (AF) is used which is fixing U (or V) andoptimizing the objective function 119869(UV) with just variablematrix V (or U) and then fixing V (or U) and optimizing119869(UV) with just U (or V) AF will be implemented alterna-tively till 119869(UV) reaches its upper bound
After the optimum process new Ulowast and Vlowast that maxi-mize 119869(UV) are got and preserve the discriminative informa-tion of original sample data119883 Based on this then the size ofthe upper layer can be determined by the number of positiveeigenvalues of Ulowast and Vlowast which is 119875 and 119876 respectively
33 Pretraining with Greedy Layer-Wise ReconstructionMethod In last subsection the size of the upper layer isdetermined to be 119875times119876 In this subsection the parameters ofthe two adjacent layers will be refined with the greedy-wisereconstruction method proposed by Hinton et al [11] To
illustrate this pretraining process we take the visible inputlayer 1198811 and the first hidden layer 1198671 for example
The visible input layer 1198811 and the first hidden layer 1198671
contract a restrict Boltzmann machine (RBM) 119868 times 119869 is theneural number in 1198811 and 119875 times 119876 is that of 1198671 The energy ofthe state (V1ℎ1) in this RBM is
119864 (k1 h1 1205791) = minus (k1Ah1 + b1k1 + c1h1)
= minus
119894le119868119895le119869
sum119894=1119895=1
119901le119875119902le119876
sum119901=1119902=1
V11198941198951198601
119894119895119901119902ℎ1
119901119902
minus
119894le119868119895le119869
sum119894=1119895=1
1198871
119894119895V1119894119895minus
119901le119875119902le119876
sum119901=1119902=1
1198881
119901119902ℎ1
119901119902
(6)
in which 1205791 = (A1 b1 c1) are the parameters between thevisible input layer 1198811 and the first hidden layer 1198671 1198601
119894119895119901119902
is the symmetric weights from input neural (119894 119895) in 1198811 tothe hidden neural (119901 119902) in 119867
1 1198871119894119895and 1198881
119901119902are the (119894 119895)
119905ℎ and(119901 119902)119905ℎ bias of 1198811 and 1198671 So this RBM is with the joint
distribution as follows
119875 (k1 h1 1205791) =1
119885119890minus119864(k1 h11205791)
=119890minus119864(k
1h11205791)
sumV1 sumℎ1 119890minus119864(k1 h11205791)
(7)
Here Z is the normalization parameter and the probabilitythat k1 is assigned to 1198811 of this modal is
119875 (k1) =1
119885sum
ℎ1
119890minus119864(k1 h1 1205791)
=sumℎ1 119890minus119864(k1 h11205791)
sumV1 sumℎ1 119890minus119864(k1 h11205791) (8)
After that the conditional distributions over visible inputstate k1 in layer 1198811 and hidden state ℎ1 in 1198671 are able to begiven by the logistic function respectively
119901 (h1 | k1) = prod119901119902
119901 (ℎ1
119901119902| k1) 119901 (ℎ
1
119901119902| k1)
= 120590(
119894le119868119895le119869
sum119894=1119895=1
V11198941198951198601
119894119895119901119902+ 1198881
119901119902)
(9)
119901 (k1 | h1) = prod119894119895
119901 (k1119894119895| h1) 119901 (k1
119894119895| h1)
= 120590(
119901le119875119902le119876
sum119901=1119902=1
ℎ1
1199011199021198601
119894119895119901119902+ 1198871
119894119895)
(10)
Here 120590(119909) = 1(1 + exp(minus119909))At last the weights and biases are able to be updated
step by step from random Gaussian distribution values
The Scientific World Journal 5
(a) (b)
Figure 3 Some positive and negative training samples (a) Positive samples (b) Negative samples
1198601119894119895119901119902
(0) 1198871119894119895(0) and 1198881
119901119902(0)with Contrastive Divergence algo-
rithm [17] and the updating formulations are
1198601
119894119895119901119902= 1205991198601
119894119895119901119902
+ 120576119860(⟨V1119894119895(0) ℎ1
119894119895(0)⟩
dataminus ⟨V1119894119895(119905) ℎ1
119894119895(119905)⟩
recon)
1198871
119894119895= 1205991198871
119894119895+ 120576119887(V1119894119895(0) minus V1
119894119895(119905))
1198881
119901119902= 1205991198881
119901119902+ 120576119888(ℎ1
119901119902(0) minus ℎ
1
119901119902(119905))
(11)
in which ⟨sdot⟩data means the expectation with respect tothe data distribution and ⟨sdot⟩recon means the reconstructiondistribution after one step Meanwhile 119905 is step size which isset to 119905 = 1 typically
Above the pretraining process is demonstrated by takingthe visible input layer 119881
1 and the first hidden layer 1198671
for example Indeed the whole pretraining process will betaken from low layer groups (1198811 1198671) to up layer groups(119867119899minus1 119867119899) one by one
34 Global Fine-Tuning In the above unsurprised pretrain-ing process the greedy layer-wise algorithm is used tolearn the 2D-DBN parameters with the information addedfrom bilinear projection In this subsection a traditionalback propagation algorithm will be used to fine-tune theparameters 120579 = [A b c] with the information of label layerLa
Since good parameters initiation has been maintained inthe pretraining process back propagation is just utilized tofinely adjust the parameters so that local optimumparameters120579lowast = [Alowast blowast clowast] can be got In this stage the learning objec-tion is to minimize the classification error [minussum
119905y119905log y119905]
where y119905and y119905are the real label and output label of data X
119905
in layer 119873
4 Experiment and Analysis
This section will take experiments on vehicle datasets todemonstrate the performance of the proposed 2D-DBNThedatasets for training are Caltech1999 database which includesimages containing 126 rear view vehicles Besides another
Table 1 Detection results of three different architectures of 2D-DBN
Classifier types Correct labeling Correct rate2D-DBN (1H) 689735 93742D-DBN (2H) 706735 96052D-DBN (3H) 695735 9456
600 vehicles in images are collected by our groups in recordedroad videos for training Meanwhile the negative samplesare chosen from 500 images not containing vehicles and thenumber of negative samples for training is 5000 Figure 3shows some of these positive and negative training samplesThe testing datasets are recorded road videoswith 735manualmarked vehicles
By using the proposed method three different architec-tures of 2D-DBN are applied They all contain one visiblelayer and one label layer but with one two and three hiddenlayers respectively In training the critical parameters of theproposed 2D-DBN in experiments are set as 120572 = 05 and120599 = 08 and image samples for training are all resized to32 times 32
The detection results of these three architectures of 2D-DBN are shown in Table 1 It can be seen that 2D-DBN withtwo hidden layers maintains the highest detection rate
The learned weights of hidden layers are shownin Figure 4
Then we compared the performance of our 2D-DBNwithmany other state-of-the-art classifiers including supportvector machine (SVM) k-nearest neighbor (KNN) neuralnetworks 1D-DBN and deep convoluted neural network(DCNN)
The detection results of these methods are shown inTable 2
From the compared results it can be concluded thatclassification methods with deep architecture for example1D-DBN DCNN and 2D-DBN are significantly better thanthose of shallow architecture for example SVM KNN andNN Moreover our proposed 2D-DBN is better than 1D-DBN and DCNN due to 2D feature input and the bilinearprojection
Finally this 2D-DBN vehicle detectionmethod is utilizedon road vehicle detection system and some of the vehicle
6 The Scientific World Journal
Learned weights on DBN layer 1
(a)
Learned weights on DBN layer 2
(b)
Figure 4 Learned weights of first hidden layer and second hidden layer on 2D-DBN (a) weights of first hidden layer and (b) weights ofsecond hidden layer
Table 2 Detection results of multiple methods
Classifier types Correct labeling Correct rateSVM 658735 8952KNN 642735 8735NN 619735 84211D-DBN 684735 9306DCNN 697735 94832D-DBN (2H) 706735 9605
sensing results in real road situation are shown in Figure 5The four rows of images are picked in daylight highwayraining day highway daylight urban and night highway withroad lamp respectively The solid green box means detectedvehicles and the dotted red boxmeans undetected vehicles orfalse detected vehiclesThe average vehicle detection time forone frame with 640 times 480 resolution is around 53ms in ourAdvantech industrial computer
Overall most of the on-road vehicles can be sensed suc-cessfully while misdetection and false detection sometimesoccurred during adverse situations such as partial occlusionand bad weather
5 Conclusion
In thiswork a novel vehicle detection algorithmbased on 2D-DBN is proposed In the algorithm the proposed 2D-DBNarchitecture uses second-order planes instead of first-ordervector as input and uses bilinear projection for retainingdiscriminative information so as to determine the size of thedeep architecture which enhances the success rate of vehicledetectionOn-road experimental results demonstrate that thesystem works well in different roads weather and lightingconditions
The future work of our researchwill focus on the situationwhen a vehicle is partially occluded with deep architectureframework
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Figure 5 Some of the real road vehicle sensing results Firstrow daylight highway situation second row raining day highwaysituation third row daylight urban situation forth row nighthighway with road lamp
Acknowledgments
This research was funded partly and was supported inpart by the National Natural Science Foundation of Chinaunder Grant no 51305167 Information Technology ResearchProgram of Transport Ministry of China under Grant no2013364836900 and Jiangsu University Scientific ResearchFoundation for Senior Professionals under Grant no12JDG010
References
[1] W Liu X Wen B Duan H Yuan and N Wang ldquoRear vehicledetection and tracking for lane change assistrdquo in Proceedings ofthe IEEE Intelligent Vehicles Symposium (IV rsquo07) pp 252ndash257June 2007
[2] R Miller Z Sun and G Bebis ldquoMonocular precrash vehicledetection features and classifiersrdquo IEEE Transactions on ImageProcessing vol 15 no 7 pp 2019ndash2034 2006
The Scientific World Journal 7
[3] S Sivaraman and M M Trivedi ldquoActive learning for on-roadvehicle detection a comparative studyrdquo Machine Vision andApplications pp 1ndash13 2011
[4] S Teoh and T Brunl ldquoSymmetry-based monocular vehicledetection systemrdquoMachine Vision and Applications vol 23 pp831ndash842 2012
[5] R Sindoori K Ravichandran and B Santhi ldquoAdaboost tech-nique for vehicle detection in aerial surveillancerdquo InternationalJournal of Engineering amp Technology vol 5 no 2 2013
[6] J Cui F Liu Z Li and Z Jia ldquoVehicle localisation using asingle camerardquo in Proceedings of the IEEE Intelligent VehiclesSymposium (IV rsquo10) pp 871ndash876 June 2010
[7] T T Son and S Mita ldquoCar detection using multi-featureselection for varying posesrdquo inProceedings of the IEEE IntelligentVehicles Symposium pp 507ndash512 June 2009
[8] D Acunzo Y Zhu B Xie and G Baratoff ldquoContext-adaptiveapproach for vehicle detection under varying lighting condi-tionsrdquo in Proceedings of the 10th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo07) pp 654ndash660October 2007
[9] C T Lin S C Hsu J F Lee et al ldquoBoosted vehicle detectionusing local and global featuresrdquo Journal of Signal amp InformationProcessing vol 4 no 3 2013
[10] O Ludwig Jr and U Nunes ldquoImproving the generalizationproperties of neural networks an application to vehicle detec-tionrdquo in Proceedings of the 11th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo08) pp 310ndash315December 2008
[11] G E Hinton S Osindero and Y-W Teh ldquoA fast learningalgorithm for deep belief netsrdquoNeural Computation vol 18 no7 pp 1527ndash1554 2006
[12] V Nair and G E Hinton ldquo3D object recognition with deepbelief netsrdquo in Proceedings of the 23rd Annual Conference onNeural Information Processing Systems (NIPS rsquo09) pp 1339ndash1347December 2009
[13] G Taylor R Fergus Y L Cun and C Bregler ldquoConvolutionallearning of spatio-temporal featuresrdquo in Computer VisionmdashECCV 2010 vol 6316 of Lecture Notes in Computer Science pp140ndash153 2010
[14] C X Zhang J S Zhang N N Ji et al ldquoLearning ensemble clas-sifiers via restricted Boltzmann machinesrdquo Pattern RecognitionLetters vol 36 pp 161ndash170 2014
[15] S-H Zhong Y Liu and Y Liu ldquoBilinear deep learning forimage classificationrdquo in Proceedings of the 19th ACM Interna-tional Conference on Multimedia ACM Multimedia (SIG MMrsquo11) December 2011
[16] S YanD Xu B ZhangH-J ZhangQ Yang and S Lin ldquoGraphembedding and extensions a general framework for dimen-sionality reductionrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 29 no 1 pp 40ndash51 2007
[17] F Wood and G E Hinton ldquoTraining products of experts byminimizing contrastive divergencerdquo Tech Rep 2012 53 143Brown University 2012
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![Page 3: Research Article A Vehicle Detection Algorithm Based on ...downloads.hindawi.com/journals/tswj/2014/647380.pdf · Vision based vehicle detection is a critical technology that plays](https://reader035.vdocument.in/reader035/viewer/2022062918/5ededf25ad6a402d666a3a51/html5/thumbnails/3.jpg)
The Scientific World Journal 3
3 Deep Learning Based Vehicle Detection
In this section a novel algorithm based on deep beliefnetwork (DBN) is proposed Traditional DBN for objectclassification has some shortages First the training samplesare regularized to first-order vector which will lead tothe missing of spatial information contained by the imagesamples This will obviously lead to a decline in the detectionrate in vehicle detection tasks Secondly the size of layers(such as node number and layer number) in the traditionalDBN is manually set which is often big and will lead tostructural redundancy and increase the training and decisiontime of the classifier while for vehicle detection algorithmwhich is usually used in real-time application decision timeis a critical factor The proposed 2D-DBN architecture forvehicle detection uses second-order planes instead of first-order vector of 1D-DBN as input and uses bilinear projectionfor retaining discriminative information so as to determinethe size of the deep architectureThe bilinear projectionmapsoriginal second-order output of lower layer to a small bilinearspace without reducing discriminative information And thesize of the upper layer is that of the bilinear space
In Section 31 the overall architecture of our 2D-DBNfor vehicle detection will be introduced In Section 32 thebilinear projectionmethod of lower layer outputwill be givenIn Sections 33 and 34 the training method of the whole 2D-DBN for vehicle detection will be deduced
31 2119863 Deep Belief Network (2D-DBN) for Vehicle DetectionLet 119883 be the set of data samples including vehicle imagesand nonvehicle images assuming that 119883 is consisting withK samples which is shown below
119883 = [X1X2 X
119896 X
119870] (1)
In 119883 X119896is training samples and in the image space R119868times119869
Meanwhile 119884 means the labels corresponding to 119883 whichcan be written as
119884 = [1199101 1199102 119910
119896 119910
119870] (2)
In 119884 119910119896is the label vector of X
119896 If X119896belongs to vehicles
119910119896= (1 0) On the contrary 119910
119896= (0 1)
The ultimate purpose in vehicle detection task is to learna mapping function from training data 119883 to the label data119884 based on the given training set so that this mappingfunction is able to classify unknown images between vehicleand nonvehicle
Based on the task described above a novel 2D deep beliefnetwork (2D-DBN) is proposed to address this problemFigure 2 shows the overall architecture of 2D-DBN A fullyinterconnected directed belief network includes one visibleinput layer 1198811 119873 hidden layers 1198671 119867119873 and one visiblelabel layer La at the top The visible input layer 1198811 maintains119872times119873 neural and equal to the dimension of training featurewhich is the original 2D image pixel values of training sam-ples in this application Sincemaximumdiscriminative abilitywishes to be preserved from layer to layer with nonredundantlayer size in this application for real-time requirement the
y1
ky2
k
La
HN
HNminus1
H1
V1
Xk
Visible layer
Feature input
Label layer
Hidden layer
Figure 2 Proposed 2D-DBN for vehicle detection
size of119873 hidden layers is dynamically decided with so-calledbilinear projection In the top the La layer just has two unitswhich is equal to the classes this application would like toclassify Till now the problem is formulated to search for theoptimum parameter space 120579 of this 2D-DBN
The main learning process of the proposed 2D-DBN hasthree steps
(1) The bilinear projection is utilized to map the lowerlayer output data onto subspace and optimized tooptimum dimension as well as to retain discrimina-tive information The size of the upper layer will bedetermined by this optimum dimension
(2) When the size of the upper layer is determined theparameters of the two adjacent layers will be refinedwith the greedy-wise reconstruction method Repeatstep (1) and step (2) till all the parameters of hiddenlayers are fixedHere step (1) and step (2) are so calledpretraining process
(3) Finally the whole 2D-DBN will be fine-tuned withthe La layer information based on back propagationHere step (3) can be viewed as supervised trainingstep
32 Bilinear Projection for Upper Layer Size DeterminationIn this section followed with Zhongrsquos contribution [15]bilinear projection is used in order to determine the size ofevery upper layer in adjacent layer groups As mentioned inSection 31 with the labeled training data X
119896isin R119868times119869 as the
output of the visible layer 1198811 bilinear projection maps the
4 The Scientific World Journal
original data X119896onto a subspace and is represented by its
latent form LX119896 The bilinear projection is written as follows
LX119896= U119879X
119896V 119896 = 1 2 119870 (3)
Here U isin 119877119872times119875 and V isin 119877119873times119876 are projection matricesthat map the original data X
119896by its latent form LX
119896with the
constraint that U119879U = I and V119879V = IHow to determine the value of U and V so that the
discriminative information ofX119896can be preserved is the issue
that needs to be solved For this a specific objective functionis built as follows
argmaxUV
119869 (UV) = sum119894119895
10038171003817100381710038171003817U119879 (X
119894minus X119895)V10038171003817100381710038171003817
2
times (120572119861119894119895minus (1 minus 120572)119882
119894119895)
(4)
in which 119861119894119895is between-class weights 119882
119894119895is within class
weights and 120572 isin [0 1] is the balance parameter 119861119894119895and 119882
119894119895
are calculated as follows [16]
119861119894119895=
minus119899119899119888
(119899119899119888
+ 119899119888) 119899119888
if 119910119888119894= 119910119888119895= 1
1
119899119899119888
+ 119899119888
else
119882119894119895=
1
119899119888
if 119910119888119894= 119910119888119895= 1
0 else
(5)
Here 119910119888119894is the class label of sample data X
119894 which is either
(1 0) or (0 1) 119899119888is the number of samples that belong to
class 119888 and 119899119888is those not belonging to class 119888 Since vehicle
detection is a binary classification problem 119888 isin 1 2 in thisapplication
It can be seen that the purpose of the objective functionis to simultaneously maximize the between-class distancesand minimize the within-class distances In other words theobjective function focuses on maximizing the discriminativeinformation of all the sample data However optimizing119869(UV) is a nonconvex optimization problem with twomatrices U and V To deal with this trouble a strategy calledalternative fixing (AF) is used which is fixing U (or V) andoptimizing the objective function 119869(UV) with just variablematrix V (or U) and then fixing V (or U) and optimizing119869(UV) with just U (or V) AF will be implemented alterna-tively till 119869(UV) reaches its upper bound
After the optimum process new Ulowast and Vlowast that maxi-mize 119869(UV) are got and preserve the discriminative informa-tion of original sample data119883 Based on this then the size ofthe upper layer can be determined by the number of positiveeigenvalues of Ulowast and Vlowast which is 119875 and 119876 respectively
33 Pretraining with Greedy Layer-Wise ReconstructionMethod In last subsection the size of the upper layer isdetermined to be 119875times119876 In this subsection the parameters ofthe two adjacent layers will be refined with the greedy-wisereconstruction method proposed by Hinton et al [11] To
illustrate this pretraining process we take the visible inputlayer 1198811 and the first hidden layer 1198671 for example
The visible input layer 1198811 and the first hidden layer 1198671
contract a restrict Boltzmann machine (RBM) 119868 times 119869 is theneural number in 1198811 and 119875 times 119876 is that of 1198671 The energy ofthe state (V1ℎ1) in this RBM is
119864 (k1 h1 1205791) = minus (k1Ah1 + b1k1 + c1h1)
= minus
119894le119868119895le119869
sum119894=1119895=1
119901le119875119902le119876
sum119901=1119902=1
V11198941198951198601
119894119895119901119902ℎ1
119901119902
minus
119894le119868119895le119869
sum119894=1119895=1
1198871
119894119895V1119894119895minus
119901le119875119902le119876
sum119901=1119902=1
1198881
119901119902ℎ1
119901119902
(6)
in which 1205791 = (A1 b1 c1) are the parameters between thevisible input layer 1198811 and the first hidden layer 1198671 1198601
119894119895119901119902
is the symmetric weights from input neural (119894 119895) in 1198811 tothe hidden neural (119901 119902) in 119867
1 1198871119894119895and 1198881
119901119902are the (119894 119895)
119905ℎ and(119901 119902)119905ℎ bias of 1198811 and 1198671 So this RBM is with the joint
distribution as follows
119875 (k1 h1 1205791) =1
119885119890minus119864(k1 h11205791)
=119890minus119864(k
1h11205791)
sumV1 sumℎ1 119890minus119864(k1 h11205791)
(7)
Here Z is the normalization parameter and the probabilitythat k1 is assigned to 1198811 of this modal is
119875 (k1) =1
119885sum
ℎ1
119890minus119864(k1 h1 1205791)
=sumℎ1 119890minus119864(k1 h11205791)
sumV1 sumℎ1 119890minus119864(k1 h11205791) (8)
After that the conditional distributions over visible inputstate k1 in layer 1198811 and hidden state ℎ1 in 1198671 are able to begiven by the logistic function respectively
119901 (h1 | k1) = prod119901119902
119901 (ℎ1
119901119902| k1) 119901 (ℎ
1
119901119902| k1)
= 120590(
119894le119868119895le119869
sum119894=1119895=1
V11198941198951198601
119894119895119901119902+ 1198881
119901119902)
(9)
119901 (k1 | h1) = prod119894119895
119901 (k1119894119895| h1) 119901 (k1
119894119895| h1)
= 120590(
119901le119875119902le119876
sum119901=1119902=1
ℎ1
1199011199021198601
119894119895119901119902+ 1198871
119894119895)
(10)
Here 120590(119909) = 1(1 + exp(minus119909))At last the weights and biases are able to be updated
step by step from random Gaussian distribution values
The Scientific World Journal 5
(a) (b)
Figure 3 Some positive and negative training samples (a) Positive samples (b) Negative samples
1198601119894119895119901119902
(0) 1198871119894119895(0) and 1198881
119901119902(0)with Contrastive Divergence algo-
rithm [17] and the updating formulations are
1198601
119894119895119901119902= 1205991198601
119894119895119901119902
+ 120576119860(⟨V1119894119895(0) ℎ1
119894119895(0)⟩
dataminus ⟨V1119894119895(119905) ℎ1
119894119895(119905)⟩
recon)
1198871
119894119895= 1205991198871
119894119895+ 120576119887(V1119894119895(0) minus V1
119894119895(119905))
1198881
119901119902= 1205991198881
119901119902+ 120576119888(ℎ1
119901119902(0) minus ℎ
1
119901119902(119905))
(11)
in which ⟨sdot⟩data means the expectation with respect tothe data distribution and ⟨sdot⟩recon means the reconstructiondistribution after one step Meanwhile 119905 is step size which isset to 119905 = 1 typically
Above the pretraining process is demonstrated by takingthe visible input layer 119881
1 and the first hidden layer 1198671
for example Indeed the whole pretraining process will betaken from low layer groups (1198811 1198671) to up layer groups(119867119899minus1 119867119899) one by one
34 Global Fine-Tuning In the above unsurprised pretrain-ing process the greedy layer-wise algorithm is used tolearn the 2D-DBN parameters with the information addedfrom bilinear projection In this subsection a traditionalback propagation algorithm will be used to fine-tune theparameters 120579 = [A b c] with the information of label layerLa
Since good parameters initiation has been maintained inthe pretraining process back propagation is just utilized tofinely adjust the parameters so that local optimumparameters120579lowast = [Alowast blowast clowast] can be got In this stage the learning objec-tion is to minimize the classification error [minussum
119905y119905log y119905]
where y119905and y119905are the real label and output label of data X
119905
in layer 119873
4 Experiment and Analysis
This section will take experiments on vehicle datasets todemonstrate the performance of the proposed 2D-DBNThedatasets for training are Caltech1999 database which includesimages containing 126 rear view vehicles Besides another
Table 1 Detection results of three different architectures of 2D-DBN
Classifier types Correct labeling Correct rate2D-DBN (1H) 689735 93742D-DBN (2H) 706735 96052D-DBN (3H) 695735 9456
600 vehicles in images are collected by our groups in recordedroad videos for training Meanwhile the negative samplesare chosen from 500 images not containing vehicles and thenumber of negative samples for training is 5000 Figure 3shows some of these positive and negative training samplesThe testing datasets are recorded road videoswith 735manualmarked vehicles
By using the proposed method three different architec-tures of 2D-DBN are applied They all contain one visiblelayer and one label layer but with one two and three hiddenlayers respectively In training the critical parameters of theproposed 2D-DBN in experiments are set as 120572 = 05 and120599 = 08 and image samples for training are all resized to32 times 32
The detection results of these three architectures of 2D-DBN are shown in Table 1 It can be seen that 2D-DBN withtwo hidden layers maintains the highest detection rate
The learned weights of hidden layers are shownin Figure 4
Then we compared the performance of our 2D-DBNwithmany other state-of-the-art classifiers including supportvector machine (SVM) k-nearest neighbor (KNN) neuralnetworks 1D-DBN and deep convoluted neural network(DCNN)
The detection results of these methods are shown inTable 2
From the compared results it can be concluded thatclassification methods with deep architecture for example1D-DBN DCNN and 2D-DBN are significantly better thanthose of shallow architecture for example SVM KNN andNN Moreover our proposed 2D-DBN is better than 1D-DBN and DCNN due to 2D feature input and the bilinearprojection
Finally this 2D-DBN vehicle detectionmethod is utilizedon road vehicle detection system and some of the vehicle
6 The Scientific World Journal
Learned weights on DBN layer 1
(a)
Learned weights on DBN layer 2
(b)
Figure 4 Learned weights of first hidden layer and second hidden layer on 2D-DBN (a) weights of first hidden layer and (b) weights ofsecond hidden layer
Table 2 Detection results of multiple methods
Classifier types Correct labeling Correct rateSVM 658735 8952KNN 642735 8735NN 619735 84211D-DBN 684735 9306DCNN 697735 94832D-DBN (2H) 706735 9605
sensing results in real road situation are shown in Figure 5The four rows of images are picked in daylight highwayraining day highway daylight urban and night highway withroad lamp respectively The solid green box means detectedvehicles and the dotted red boxmeans undetected vehicles orfalse detected vehiclesThe average vehicle detection time forone frame with 640 times 480 resolution is around 53ms in ourAdvantech industrial computer
Overall most of the on-road vehicles can be sensed suc-cessfully while misdetection and false detection sometimesoccurred during adverse situations such as partial occlusionand bad weather
5 Conclusion
In thiswork a novel vehicle detection algorithmbased on 2D-DBN is proposed In the algorithm the proposed 2D-DBNarchitecture uses second-order planes instead of first-ordervector as input and uses bilinear projection for retainingdiscriminative information so as to determine the size of thedeep architecture which enhances the success rate of vehicledetectionOn-road experimental results demonstrate that thesystem works well in different roads weather and lightingconditions
The future work of our researchwill focus on the situationwhen a vehicle is partially occluded with deep architectureframework
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Figure 5 Some of the real road vehicle sensing results Firstrow daylight highway situation second row raining day highwaysituation third row daylight urban situation forth row nighthighway with road lamp
Acknowledgments
This research was funded partly and was supported inpart by the National Natural Science Foundation of Chinaunder Grant no 51305167 Information Technology ResearchProgram of Transport Ministry of China under Grant no2013364836900 and Jiangsu University Scientific ResearchFoundation for Senior Professionals under Grant no12JDG010
References
[1] W Liu X Wen B Duan H Yuan and N Wang ldquoRear vehicledetection and tracking for lane change assistrdquo in Proceedings ofthe IEEE Intelligent Vehicles Symposium (IV rsquo07) pp 252ndash257June 2007
[2] R Miller Z Sun and G Bebis ldquoMonocular precrash vehicledetection features and classifiersrdquo IEEE Transactions on ImageProcessing vol 15 no 7 pp 2019ndash2034 2006
The Scientific World Journal 7
[3] S Sivaraman and M M Trivedi ldquoActive learning for on-roadvehicle detection a comparative studyrdquo Machine Vision andApplications pp 1ndash13 2011
[4] S Teoh and T Brunl ldquoSymmetry-based monocular vehicledetection systemrdquoMachine Vision and Applications vol 23 pp831ndash842 2012
[5] R Sindoori K Ravichandran and B Santhi ldquoAdaboost tech-nique for vehicle detection in aerial surveillancerdquo InternationalJournal of Engineering amp Technology vol 5 no 2 2013
[6] J Cui F Liu Z Li and Z Jia ldquoVehicle localisation using asingle camerardquo in Proceedings of the IEEE Intelligent VehiclesSymposium (IV rsquo10) pp 871ndash876 June 2010
[7] T T Son and S Mita ldquoCar detection using multi-featureselection for varying posesrdquo inProceedings of the IEEE IntelligentVehicles Symposium pp 507ndash512 June 2009
[8] D Acunzo Y Zhu B Xie and G Baratoff ldquoContext-adaptiveapproach for vehicle detection under varying lighting condi-tionsrdquo in Proceedings of the 10th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo07) pp 654ndash660October 2007
[9] C T Lin S C Hsu J F Lee et al ldquoBoosted vehicle detectionusing local and global featuresrdquo Journal of Signal amp InformationProcessing vol 4 no 3 2013
[10] O Ludwig Jr and U Nunes ldquoImproving the generalizationproperties of neural networks an application to vehicle detec-tionrdquo in Proceedings of the 11th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo08) pp 310ndash315December 2008
[11] G E Hinton S Osindero and Y-W Teh ldquoA fast learningalgorithm for deep belief netsrdquoNeural Computation vol 18 no7 pp 1527ndash1554 2006
[12] V Nair and G E Hinton ldquo3D object recognition with deepbelief netsrdquo in Proceedings of the 23rd Annual Conference onNeural Information Processing Systems (NIPS rsquo09) pp 1339ndash1347December 2009
[13] G Taylor R Fergus Y L Cun and C Bregler ldquoConvolutionallearning of spatio-temporal featuresrdquo in Computer VisionmdashECCV 2010 vol 6316 of Lecture Notes in Computer Science pp140ndash153 2010
[14] C X Zhang J S Zhang N N Ji et al ldquoLearning ensemble clas-sifiers via restricted Boltzmann machinesrdquo Pattern RecognitionLetters vol 36 pp 161ndash170 2014
[15] S-H Zhong Y Liu and Y Liu ldquoBilinear deep learning forimage classificationrdquo in Proceedings of the 19th ACM Interna-tional Conference on Multimedia ACM Multimedia (SIG MMrsquo11) December 2011
[16] S YanD Xu B ZhangH-J ZhangQ Yang and S Lin ldquoGraphembedding and extensions a general framework for dimen-sionality reductionrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 29 no 1 pp 40ndash51 2007
[17] F Wood and G E Hinton ldquoTraining products of experts byminimizing contrastive divergencerdquo Tech Rep 2012 53 143Brown University 2012
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![Page 4: Research Article A Vehicle Detection Algorithm Based on ...downloads.hindawi.com/journals/tswj/2014/647380.pdf · Vision based vehicle detection is a critical technology that plays](https://reader035.vdocument.in/reader035/viewer/2022062918/5ededf25ad6a402d666a3a51/html5/thumbnails/4.jpg)
4 The Scientific World Journal
original data X119896onto a subspace and is represented by its
latent form LX119896 The bilinear projection is written as follows
LX119896= U119879X
119896V 119896 = 1 2 119870 (3)
Here U isin 119877119872times119875 and V isin 119877119873times119876 are projection matricesthat map the original data X
119896by its latent form LX
119896with the
constraint that U119879U = I and V119879V = IHow to determine the value of U and V so that the
discriminative information ofX119896can be preserved is the issue
that needs to be solved For this a specific objective functionis built as follows
argmaxUV
119869 (UV) = sum119894119895
10038171003817100381710038171003817U119879 (X
119894minus X119895)V10038171003817100381710038171003817
2
times (120572119861119894119895minus (1 minus 120572)119882
119894119895)
(4)
in which 119861119894119895is between-class weights 119882
119894119895is within class
weights and 120572 isin [0 1] is the balance parameter 119861119894119895and 119882
119894119895
are calculated as follows [16]
119861119894119895=
minus119899119899119888
(119899119899119888
+ 119899119888) 119899119888
if 119910119888119894= 119910119888119895= 1
1
119899119899119888
+ 119899119888
else
119882119894119895=
1
119899119888
if 119910119888119894= 119910119888119895= 1
0 else
(5)
Here 119910119888119894is the class label of sample data X
119894 which is either
(1 0) or (0 1) 119899119888is the number of samples that belong to
class 119888 and 119899119888is those not belonging to class 119888 Since vehicle
detection is a binary classification problem 119888 isin 1 2 in thisapplication
It can be seen that the purpose of the objective functionis to simultaneously maximize the between-class distancesand minimize the within-class distances In other words theobjective function focuses on maximizing the discriminativeinformation of all the sample data However optimizing119869(UV) is a nonconvex optimization problem with twomatrices U and V To deal with this trouble a strategy calledalternative fixing (AF) is used which is fixing U (or V) andoptimizing the objective function 119869(UV) with just variablematrix V (or U) and then fixing V (or U) and optimizing119869(UV) with just U (or V) AF will be implemented alterna-tively till 119869(UV) reaches its upper bound
After the optimum process new Ulowast and Vlowast that maxi-mize 119869(UV) are got and preserve the discriminative informa-tion of original sample data119883 Based on this then the size ofthe upper layer can be determined by the number of positiveeigenvalues of Ulowast and Vlowast which is 119875 and 119876 respectively
33 Pretraining with Greedy Layer-Wise ReconstructionMethod In last subsection the size of the upper layer isdetermined to be 119875times119876 In this subsection the parameters ofthe two adjacent layers will be refined with the greedy-wisereconstruction method proposed by Hinton et al [11] To
illustrate this pretraining process we take the visible inputlayer 1198811 and the first hidden layer 1198671 for example
The visible input layer 1198811 and the first hidden layer 1198671
contract a restrict Boltzmann machine (RBM) 119868 times 119869 is theneural number in 1198811 and 119875 times 119876 is that of 1198671 The energy ofthe state (V1ℎ1) in this RBM is
119864 (k1 h1 1205791) = minus (k1Ah1 + b1k1 + c1h1)
= minus
119894le119868119895le119869
sum119894=1119895=1
119901le119875119902le119876
sum119901=1119902=1
V11198941198951198601
119894119895119901119902ℎ1
119901119902
minus
119894le119868119895le119869
sum119894=1119895=1
1198871
119894119895V1119894119895minus
119901le119875119902le119876
sum119901=1119902=1
1198881
119901119902ℎ1
119901119902
(6)
in which 1205791 = (A1 b1 c1) are the parameters between thevisible input layer 1198811 and the first hidden layer 1198671 1198601
119894119895119901119902
is the symmetric weights from input neural (119894 119895) in 1198811 tothe hidden neural (119901 119902) in 119867
1 1198871119894119895and 1198881
119901119902are the (119894 119895)
119905ℎ and(119901 119902)119905ℎ bias of 1198811 and 1198671 So this RBM is with the joint
distribution as follows
119875 (k1 h1 1205791) =1
119885119890minus119864(k1 h11205791)
=119890minus119864(k
1h11205791)
sumV1 sumℎ1 119890minus119864(k1 h11205791)
(7)
Here Z is the normalization parameter and the probabilitythat k1 is assigned to 1198811 of this modal is
119875 (k1) =1
119885sum
ℎ1
119890minus119864(k1 h1 1205791)
=sumℎ1 119890minus119864(k1 h11205791)
sumV1 sumℎ1 119890minus119864(k1 h11205791) (8)
After that the conditional distributions over visible inputstate k1 in layer 1198811 and hidden state ℎ1 in 1198671 are able to begiven by the logistic function respectively
119901 (h1 | k1) = prod119901119902
119901 (ℎ1
119901119902| k1) 119901 (ℎ
1
119901119902| k1)
= 120590(
119894le119868119895le119869
sum119894=1119895=1
V11198941198951198601
119894119895119901119902+ 1198881
119901119902)
(9)
119901 (k1 | h1) = prod119894119895
119901 (k1119894119895| h1) 119901 (k1
119894119895| h1)
= 120590(
119901le119875119902le119876
sum119901=1119902=1
ℎ1
1199011199021198601
119894119895119901119902+ 1198871
119894119895)
(10)
Here 120590(119909) = 1(1 + exp(minus119909))At last the weights and biases are able to be updated
step by step from random Gaussian distribution values
The Scientific World Journal 5
(a) (b)
Figure 3 Some positive and negative training samples (a) Positive samples (b) Negative samples
1198601119894119895119901119902
(0) 1198871119894119895(0) and 1198881
119901119902(0)with Contrastive Divergence algo-
rithm [17] and the updating formulations are
1198601
119894119895119901119902= 1205991198601
119894119895119901119902
+ 120576119860(⟨V1119894119895(0) ℎ1
119894119895(0)⟩
dataminus ⟨V1119894119895(119905) ℎ1
119894119895(119905)⟩
recon)
1198871
119894119895= 1205991198871
119894119895+ 120576119887(V1119894119895(0) minus V1
119894119895(119905))
1198881
119901119902= 1205991198881
119901119902+ 120576119888(ℎ1
119901119902(0) minus ℎ
1
119901119902(119905))
(11)
in which ⟨sdot⟩data means the expectation with respect tothe data distribution and ⟨sdot⟩recon means the reconstructiondistribution after one step Meanwhile 119905 is step size which isset to 119905 = 1 typically
Above the pretraining process is demonstrated by takingthe visible input layer 119881
1 and the first hidden layer 1198671
for example Indeed the whole pretraining process will betaken from low layer groups (1198811 1198671) to up layer groups(119867119899minus1 119867119899) one by one
34 Global Fine-Tuning In the above unsurprised pretrain-ing process the greedy layer-wise algorithm is used tolearn the 2D-DBN parameters with the information addedfrom bilinear projection In this subsection a traditionalback propagation algorithm will be used to fine-tune theparameters 120579 = [A b c] with the information of label layerLa
Since good parameters initiation has been maintained inthe pretraining process back propagation is just utilized tofinely adjust the parameters so that local optimumparameters120579lowast = [Alowast blowast clowast] can be got In this stage the learning objec-tion is to minimize the classification error [minussum
119905y119905log y119905]
where y119905and y119905are the real label and output label of data X
119905
in layer 119873
4 Experiment and Analysis
This section will take experiments on vehicle datasets todemonstrate the performance of the proposed 2D-DBNThedatasets for training are Caltech1999 database which includesimages containing 126 rear view vehicles Besides another
Table 1 Detection results of three different architectures of 2D-DBN
Classifier types Correct labeling Correct rate2D-DBN (1H) 689735 93742D-DBN (2H) 706735 96052D-DBN (3H) 695735 9456
600 vehicles in images are collected by our groups in recordedroad videos for training Meanwhile the negative samplesare chosen from 500 images not containing vehicles and thenumber of negative samples for training is 5000 Figure 3shows some of these positive and negative training samplesThe testing datasets are recorded road videoswith 735manualmarked vehicles
By using the proposed method three different architec-tures of 2D-DBN are applied They all contain one visiblelayer and one label layer but with one two and three hiddenlayers respectively In training the critical parameters of theproposed 2D-DBN in experiments are set as 120572 = 05 and120599 = 08 and image samples for training are all resized to32 times 32
The detection results of these three architectures of 2D-DBN are shown in Table 1 It can be seen that 2D-DBN withtwo hidden layers maintains the highest detection rate
The learned weights of hidden layers are shownin Figure 4
Then we compared the performance of our 2D-DBNwithmany other state-of-the-art classifiers including supportvector machine (SVM) k-nearest neighbor (KNN) neuralnetworks 1D-DBN and deep convoluted neural network(DCNN)
The detection results of these methods are shown inTable 2
From the compared results it can be concluded thatclassification methods with deep architecture for example1D-DBN DCNN and 2D-DBN are significantly better thanthose of shallow architecture for example SVM KNN andNN Moreover our proposed 2D-DBN is better than 1D-DBN and DCNN due to 2D feature input and the bilinearprojection
Finally this 2D-DBN vehicle detectionmethod is utilizedon road vehicle detection system and some of the vehicle
6 The Scientific World Journal
Learned weights on DBN layer 1
(a)
Learned weights on DBN layer 2
(b)
Figure 4 Learned weights of first hidden layer and second hidden layer on 2D-DBN (a) weights of first hidden layer and (b) weights ofsecond hidden layer
Table 2 Detection results of multiple methods
Classifier types Correct labeling Correct rateSVM 658735 8952KNN 642735 8735NN 619735 84211D-DBN 684735 9306DCNN 697735 94832D-DBN (2H) 706735 9605
sensing results in real road situation are shown in Figure 5The four rows of images are picked in daylight highwayraining day highway daylight urban and night highway withroad lamp respectively The solid green box means detectedvehicles and the dotted red boxmeans undetected vehicles orfalse detected vehiclesThe average vehicle detection time forone frame with 640 times 480 resolution is around 53ms in ourAdvantech industrial computer
Overall most of the on-road vehicles can be sensed suc-cessfully while misdetection and false detection sometimesoccurred during adverse situations such as partial occlusionand bad weather
5 Conclusion
In thiswork a novel vehicle detection algorithmbased on 2D-DBN is proposed In the algorithm the proposed 2D-DBNarchitecture uses second-order planes instead of first-ordervector as input and uses bilinear projection for retainingdiscriminative information so as to determine the size of thedeep architecture which enhances the success rate of vehicledetectionOn-road experimental results demonstrate that thesystem works well in different roads weather and lightingconditions
The future work of our researchwill focus on the situationwhen a vehicle is partially occluded with deep architectureframework
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Figure 5 Some of the real road vehicle sensing results Firstrow daylight highway situation second row raining day highwaysituation third row daylight urban situation forth row nighthighway with road lamp
Acknowledgments
This research was funded partly and was supported inpart by the National Natural Science Foundation of Chinaunder Grant no 51305167 Information Technology ResearchProgram of Transport Ministry of China under Grant no2013364836900 and Jiangsu University Scientific ResearchFoundation for Senior Professionals under Grant no12JDG010
References
[1] W Liu X Wen B Duan H Yuan and N Wang ldquoRear vehicledetection and tracking for lane change assistrdquo in Proceedings ofthe IEEE Intelligent Vehicles Symposium (IV rsquo07) pp 252ndash257June 2007
[2] R Miller Z Sun and G Bebis ldquoMonocular precrash vehicledetection features and classifiersrdquo IEEE Transactions on ImageProcessing vol 15 no 7 pp 2019ndash2034 2006
The Scientific World Journal 7
[3] S Sivaraman and M M Trivedi ldquoActive learning for on-roadvehicle detection a comparative studyrdquo Machine Vision andApplications pp 1ndash13 2011
[4] S Teoh and T Brunl ldquoSymmetry-based monocular vehicledetection systemrdquoMachine Vision and Applications vol 23 pp831ndash842 2012
[5] R Sindoori K Ravichandran and B Santhi ldquoAdaboost tech-nique for vehicle detection in aerial surveillancerdquo InternationalJournal of Engineering amp Technology vol 5 no 2 2013
[6] J Cui F Liu Z Li and Z Jia ldquoVehicle localisation using asingle camerardquo in Proceedings of the IEEE Intelligent VehiclesSymposium (IV rsquo10) pp 871ndash876 June 2010
[7] T T Son and S Mita ldquoCar detection using multi-featureselection for varying posesrdquo inProceedings of the IEEE IntelligentVehicles Symposium pp 507ndash512 June 2009
[8] D Acunzo Y Zhu B Xie and G Baratoff ldquoContext-adaptiveapproach for vehicle detection under varying lighting condi-tionsrdquo in Proceedings of the 10th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo07) pp 654ndash660October 2007
[9] C T Lin S C Hsu J F Lee et al ldquoBoosted vehicle detectionusing local and global featuresrdquo Journal of Signal amp InformationProcessing vol 4 no 3 2013
[10] O Ludwig Jr and U Nunes ldquoImproving the generalizationproperties of neural networks an application to vehicle detec-tionrdquo in Proceedings of the 11th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo08) pp 310ndash315December 2008
[11] G E Hinton S Osindero and Y-W Teh ldquoA fast learningalgorithm for deep belief netsrdquoNeural Computation vol 18 no7 pp 1527ndash1554 2006
[12] V Nair and G E Hinton ldquo3D object recognition with deepbelief netsrdquo in Proceedings of the 23rd Annual Conference onNeural Information Processing Systems (NIPS rsquo09) pp 1339ndash1347December 2009
[13] G Taylor R Fergus Y L Cun and C Bregler ldquoConvolutionallearning of spatio-temporal featuresrdquo in Computer VisionmdashECCV 2010 vol 6316 of Lecture Notes in Computer Science pp140ndash153 2010
[14] C X Zhang J S Zhang N N Ji et al ldquoLearning ensemble clas-sifiers via restricted Boltzmann machinesrdquo Pattern RecognitionLetters vol 36 pp 161ndash170 2014
[15] S-H Zhong Y Liu and Y Liu ldquoBilinear deep learning forimage classificationrdquo in Proceedings of the 19th ACM Interna-tional Conference on Multimedia ACM Multimedia (SIG MMrsquo11) December 2011
[16] S YanD Xu B ZhangH-J ZhangQ Yang and S Lin ldquoGraphembedding and extensions a general framework for dimen-sionality reductionrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 29 no 1 pp 40ndash51 2007
[17] F Wood and G E Hinton ldquoTraining products of experts byminimizing contrastive divergencerdquo Tech Rep 2012 53 143Brown University 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
![Page 5: Research Article A Vehicle Detection Algorithm Based on ...downloads.hindawi.com/journals/tswj/2014/647380.pdf · Vision based vehicle detection is a critical technology that plays](https://reader035.vdocument.in/reader035/viewer/2022062918/5ededf25ad6a402d666a3a51/html5/thumbnails/5.jpg)
The Scientific World Journal 5
(a) (b)
Figure 3 Some positive and negative training samples (a) Positive samples (b) Negative samples
1198601119894119895119901119902
(0) 1198871119894119895(0) and 1198881
119901119902(0)with Contrastive Divergence algo-
rithm [17] and the updating formulations are
1198601
119894119895119901119902= 1205991198601
119894119895119901119902
+ 120576119860(⟨V1119894119895(0) ℎ1
119894119895(0)⟩
dataminus ⟨V1119894119895(119905) ℎ1
119894119895(119905)⟩
recon)
1198871
119894119895= 1205991198871
119894119895+ 120576119887(V1119894119895(0) minus V1
119894119895(119905))
1198881
119901119902= 1205991198881
119901119902+ 120576119888(ℎ1
119901119902(0) minus ℎ
1
119901119902(119905))
(11)
in which ⟨sdot⟩data means the expectation with respect tothe data distribution and ⟨sdot⟩recon means the reconstructiondistribution after one step Meanwhile 119905 is step size which isset to 119905 = 1 typically
Above the pretraining process is demonstrated by takingthe visible input layer 119881
1 and the first hidden layer 1198671
for example Indeed the whole pretraining process will betaken from low layer groups (1198811 1198671) to up layer groups(119867119899minus1 119867119899) one by one
34 Global Fine-Tuning In the above unsurprised pretrain-ing process the greedy layer-wise algorithm is used tolearn the 2D-DBN parameters with the information addedfrom bilinear projection In this subsection a traditionalback propagation algorithm will be used to fine-tune theparameters 120579 = [A b c] with the information of label layerLa
Since good parameters initiation has been maintained inthe pretraining process back propagation is just utilized tofinely adjust the parameters so that local optimumparameters120579lowast = [Alowast blowast clowast] can be got In this stage the learning objec-tion is to minimize the classification error [minussum
119905y119905log y119905]
where y119905and y119905are the real label and output label of data X
119905
in layer 119873
4 Experiment and Analysis
This section will take experiments on vehicle datasets todemonstrate the performance of the proposed 2D-DBNThedatasets for training are Caltech1999 database which includesimages containing 126 rear view vehicles Besides another
Table 1 Detection results of three different architectures of 2D-DBN
Classifier types Correct labeling Correct rate2D-DBN (1H) 689735 93742D-DBN (2H) 706735 96052D-DBN (3H) 695735 9456
600 vehicles in images are collected by our groups in recordedroad videos for training Meanwhile the negative samplesare chosen from 500 images not containing vehicles and thenumber of negative samples for training is 5000 Figure 3shows some of these positive and negative training samplesThe testing datasets are recorded road videoswith 735manualmarked vehicles
By using the proposed method three different architec-tures of 2D-DBN are applied They all contain one visiblelayer and one label layer but with one two and three hiddenlayers respectively In training the critical parameters of theproposed 2D-DBN in experiments are set as 120572 = 05 and120599 = 08 and image samples for training are all resized to32 times 32
The detection results of these three architectures of 2D-DBN are shown in Table 1 It can be seen that 2D-DBN withtwo hidden layers maintains the highest detection rate
The learned weights of hidden layers are shownin Figure 4
Then we compared the performance of our 2D-DBNwithmany other state-of-the-art classifiers including supportvector machine (SVM) k-nearest neighbor (KNN) neuralnetworks 1D-DBN and deep convoluted neural network(DCNN)
The detection results of these methods are shown inTable 2
From the compared results it can be concluded thatclassification methods with deep architecture for example1D-DBN DCNN and 2D-DBN are significantly better thanthose of shallow architecture for example SVM KNN andNN Moreover our proposed 2D-DBN is better than 1D-DBN and DCNN due to 2D feature input and the bilinearprojection
Finally this 2D-DBN vehicle detectionmethod is utilizedon road vehicle detection system and some of the vehicle
6 The Scientific World Journal
Learned weights on DBN layer 1
(a)
Learned weights on DBN layer 2
(b)
Figure 4 Learned weights of first hidden layer and second hidden layer on 2D-DBN (a) weights of first hidden layer and (b) weights ofsecond hidden layer
Table 2 Detection results of multiple methods
Classifier types Correct labeling Correct rateSVM 658735 8952KNN 642735 8735NN 619735 84211D-DBN 684735 9306DCNN 697735 94832D-DBN (2H) 706735 9605
sensing results in real road situation are shown in Figure 5The four rows of images are picked in daylight highwayraining day highway daylight urban and night highway withroad lamp respectively The solid green box means detectedvehicles and the dotted red boxmeans undetected vehicles orfalse detected vehiclesThe average vehicle detection time forone frame with 640 times 480 resolution is around 53ms in ourAdvantech industrial computer
Overall most of the on-road vehicles can be sensed suc-cessfully while misdetection and false detection sometimesoccurred during adverse situations such as partial occlusionand bad weather
5 Conclusion
In thiswork a novel vehicle detection algorithmbased on 2D-DBN is proposed In the algorithm the proposed 2D-DBNarchitecture uses second-order planes instead of first-ordervector as input and uses bilinear projection for retainingdiscriminative information so as to determine the size of thedeep architecture which enhances the success rate of vehicledetectionOn-road experimental results demonstrate that thesystem works well in different roads weather and lightingconditions
The future work of our researchwill focus on the situationwhen a vehicle is partially occluded with deep architectureframework
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Figure 5 Some of the real road vehicle sensing results Firstrow daylight highway situation second row raining day highwaysituation third row daylight urban situation forth row nighthighway with road lamp
Acknowledgments
This research was funded partly and was supported inpart by the National Natural Science Foundation of Chinaunder Grant no 51305167 Information Technology ResearchProgram of Transport Ministry of China under Grant no2013364836900 and Jiangsu University Scientific ResearchFoundation for Senior Professionals under Grant no12JDG010
References
[1] W Liu X Wen B Duan H Yuan and N Wang ldquoRear vehicledetection and tracking for lane change assistrdquo in Proceedings ofthe IEEE Intelligent Vehicles Symposium (IV rsquo07) pp 252ndash257June 2007
[2] R Miller Z Sun and G Bebis ldquoMonocular precrash vehicledetection features and classifiersrdquo IEEE Transactions on ImageProcessing vol 15 no 7 pp 2019ndash2034 2006
The Scientific World Journal 7
[3] S Sivaraman and M M Trivedi ldquoActive learning for on-roadvehicle detection a comparative studyrdquo Machine Vision andApplications pp 1ndash13 2011
[4] S Teoh and T Brunl ldquoSymmetry-based monocular vehicledetection systemrdquoMachine Vision and Applications vol 23 pp831ndash842 2012
[5] R Sindoori K Ravichandran and B Santhi ldquoAdaboost tech-nique for vehicle detection in aerial surveillancerdquo InternationalJournal of Engineering amp Technology vol 5 no 2 2013
[6] J Cui F Liu Z Li and Z Jia ldquoVehicle localisation using asingle camerardquo in Proceedings of the IEEE Intelligent VehiclesSymposium (IV rsquo10) pp 871ndash876 June 2010
[7] T T Son and S Mita ldquoCar detection using multi-featureselection for varying posesrdquo inProceedings of the IEEE IntelligentVehicles Symposium pp 507ndash512 June 2009
[8] D Acunzo Y Zhu B Xie and G Baratoff ldquoContext-adaptiveapproach for vehicle detection under varying lighting condi-tionsrdquo in Proceedings of the 10th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo07) pp 654ndash660October 2007
[9] C T Lin S C Hsu J F Lee et al ldquoBoosted vehicle detectionusing local and global featuresrdquo Journal of Signal amp InformationProcessing vol 4 no 3 2013
[10] O Ludwig Jr and U Nunes ldquoImproving the generalizationproperties of neural networks an application to vehicle detec-tionrdquo in Proceedings of the 11th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo08) pp 310ndash315December 2008
[11] G E Hinton S Osindero and Y-W Teh ldquoA fast learningalgorithm for deep belief netsrdquoNeural Computation vol 18 no7 pp 1527ndash1554 2006
[12] V Nair and G E Hinton ldquo3D object recognition with deepbelief netsrdquo in Proceedings of the 23rd Annual Conference onNeural Information Processing Systems (NIPS rsquo09) pp 1339ndash1347December 2009
[13] G Taylor R Fergus Y L Cun and C Bregler ldquoConvolutionallearning of spatio-temporal featuresrdquo in Computer VisionmdashECCV 2010 vol 6316 of Lecture Notes in Computer Science pp140ndash153 2010
[14] C X Zhang J S Zhang N N Ji et al ldquoLearning ensemble clas-sifiers via restricted Boltzmann machinesrdquo Pattern RecognitionLetters vol 36 pp 161ndash170 2014
[15] S-H Zhong Y Liu and Y Liu ldquoBilinear deep learning forimage classificationrdquo in Proceedings of the 19th ACM Interna-tional Conference on Multimedia ACM Multimedia (SIG MMrsquo11) December 2011
[16] S YanD Xu B ZhangH-J ZhangQ Yang and S Lin ldquoGraphembedding and extensions a general framework for dimen-sionality reductionrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 29 no 1 pp 40ndash51 2007
[17] F Wood and G E Hinton ldquoTraining products of experts byminimizing contrastive divergencerdquo Tech Rep 2012 53 143Brown University 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
![Page 6: Research Article A Vehicle Detection Algorithm Based on ...downloads.hindawi.com/journals/tswj/2014/647380.pdf · Vision based vehicle detection is a critical technology that plays](https://reader035.vdocument.in/reader035/viewer/2022062918/5ededf25ad6a402d666a3a51/html5/thumbnails/6.jpg)
6 The Scientific World Journal
Learned weights on DBN layer 1
(a)
Learned weights on DBN layer 2
(b)
Figure 4 Learned weights of first hidden layer and second hidden layer on 2D-DBN (a) weights of first hidden layer and (b) weights ofsecond hidden layer
Table 2 Detection results of multiple methods
Classifier types Correct labeling Correct rateSVM 658735 8952KNN 642735 8735NN 619735 84211D-DBN 684735 9306DCNN 697735 94832D-DBN (2H) 706735 9605
sensing results in real road situation are shown in Figure 5The four rows of images are picked in daylight highwayraining day highway daylight urban and night highway withroad lamp respectively The solid green box means detectedvehicles and the dotted red boxmeans undetected vehicles orfalse detected vehiclesThe average vehicle detection time forone frame with 640 times 480 resolution is around 53ms in ourAdvantech industrial computer
Overall most of the on-road vehicles can be sensed suc-cessfully while misdetection and false detection sometimesoccurred during adverse situations such as partial occlusionand bad weather
5 Conclusion
In thiswork a novel vehicle detection algorithmbased on 2D-DBN is proposed In the algorithm the proposed 2D-DBNarchitecture uses second-order planes instead of first-ordervector as input and uses bilinear projection for retainingdiscriminative information so as to determine the size of thedeep architecture which enhances the success rate of vehicledetectionOn-road experimental results demonstrate that thesystem works well in different roads weather and lightingconditions
The future work of our researchwill focus on the situationwhen a vehicle is partially occluded with deep architectureframework
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Figure 5 Some of the real road vehicle sensing results Firstrow daylight highway situation second row raining day highwaysituation third row daylight urban situation forth row nighthighway with road lamp
Acknowledgments
This research was funded partly and was supported inpart by the National Natural Science Foundation of Chinaunder Grant no 51305167 Information Technology ResearchProgram of Transport Ministry of China under Grant no2013364836900 and Jiangsu University Scientific ResearchFoundation for Senior Professionals under Grant no12JDG010
References
[1] W Liu X Wen B Duan H Yuan and N Wang ldquoRear vehicledetection and tracking for lane change assistrdquo in Proceedings ofthe IEEE Intelligent Vehicles Symposium (IV rsquo07) pp 252ndash257June 2007
[2] R Miller Z Sun and G Bebis ldquoMonocular precrash vehicledetection features and classifiersrdquo IEEE Transactions on ImageProcessing vol 15 no 7 pp 2019ndash2034 2006
The Scientific World Journal 7
[3] S Sivaraman and M M Trivedi ldquoActive learning for on-roadvehicle detection a comparative studyrdquo Machine Vision andApplications pp 1ndash13 2011
[4] S Teoh and T Brunl ldquoSymmetry-based monocular vehicledetection systemrdquoMachine Vision and Applications vol 23 pp831ndash842 2012
[5] R Sindoori K Ravichandran and B Santhi ldquoAdaboost tech-nique for vehicle detection in aerial surveillancerdquo InternationalJournal of Engineering amp Technology vol 5 no 2 2013
[6] J Cui F Liu Z Li and Z Jia ldquoVehicle localisation using asingle camerardquo in Proceedings of the IEEE Intelligent VehiclesSymposium (IV rsquo10) pp 871ndash876 June 2010
[7] T T Son and S Mita ldquoCar detection using multi-featureselection for varying posesrdquo inProceedings of the IEEE IntelligentVehicles Symposium pp 507ndash512 June 2009
[8] D Acunzo Y Zhu B Xie and G Baratoff ldquoContext-adaptiveapproach for vehicle detection under varying lighting condi-tionsrdquo in Proceedings of the 10th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo07) pp 654ndash660October 2007
[9] C T Lin S C Hsu J F Lee et al ldquoBoosted vehicle detectionusing local and global featuresrdquo Journal of Signal amp InformationProcessing vol 4 no 3 2013
[10] O Ludwig Jr and U Nunes ldquoImproving the generalizationproperties of neural networks an application to vehicle detec-tionrdquo in Proceedings of the 11th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo08) pp 310ndash315December 2008
[11] G E Hinton S Osindero and Y-W Teh ldquoA fast learningalgorithm for deep belief netsrdquoNeural Computation vol 18 no7 pp 1527ndash1554 2006
[12] V Nair and G E Hinton ldquo3D object recognition with deepbelief netsrdquo in Proceedings of the 23rd Annual Conference onNeural Information Processing Systems (NIPS rsquo09) pp 1339ndash1347December 2009
[13] G Taylor R Fergus Y L Cun and C Bregler ldquoConvolutionallearning of spatio-temporal featuresrdquo in Computer VisionmdashECCV 2010 vol 6316 of Lecture Notes in Computer Science pp140ndash153 2010
[14] C X Zhang J S Zhang N N Ji et al ldquoLearning ensemble clas-sifiers via restricted Boltzmann machinesrdquo Pattern RecognitionLetters vol 36 pp 161ndash170 2014
[15] S-H Zhong Y Liu and Y Liu ldquoBilinear deep learning forimage classificationrdquo in Proceedings of the 19th ACM Interna-tional Conference on Multimedia ACM Multimedia (SIG MMrsquo11) December 2011
[16] S YanD Xu B ZhangH-J ZhangQ Yang and S Lin ldquoGraphembedding and extensions a general framework for dimen-sionality reductionrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 29 no 1 pp 40ndash51 2007
[17] F Wood and G E Hinton ldquoTraining products of experts byminimizing contrastive divergencerdquo Tech Rep 2012 53 143Brown University 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational Intelligence and Neuroscience
Industrial EngineeringJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Human-ComputerInteraction
Advances in
Computer EngineeringAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
![Page 7: Research Article A Vehicle Detection Algorithm Based on ...downloads.hindawi.com/journals/tswj/2014/647380.pdf · Vision based vehicle detection is a critical technology that plays](https://reader035.vdocument.in/reader035/viewer/2022062918/5ededf25ad6a402d666a3a51/html5/thumbnails/7.jpg)
The Scientific World Journal 7
[3] S Sivaraman and M M Trivedi ldquoActive learning for on-roadvehicle detection a comparative studyrdquo Machine Vision andApplications pp 1ndash13 2011
[4] S Teoh and T Brunl ldquoSymmetry-based monocular vehicledetection systemrdquoMachine Vision and Applications vol 23 pp831ndash842 2012
[5] R Sindoori K Ravichandran and B Santhi ldquoAdaboost tech-nique for vehicle detection in aerial surveillancerdquo InternationalJournal of Engineering amp Technology vol 5 no 2 2013
[6] J Cui F Liu Z Li and Z Jia ldquoVehicle localisation using asingle camerardquo in Proceedings of the IEEE Intelligent VehiclesSymposium (IV rsquo10) pp 871ndash876 June 2010
[7] T T Son and S Mita ldquoCar detection using multi-featureselection for varying posesrdquo inProceedings of the IEEE IntelligentVehicles Symposium pp 507ndash512 June 2009
[8] D Acunzo Y Zhu B Xie and G Baratoff ldquoContext-adaptiveapproach for vehicle detection under varying lighting condi-tionsrdquo in Proceedings of the 10th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo07) pp 654ndash660October 2007
[9] C T Lin S C Hsu J F Lee et al ldquoBoosted vehicle detectionusing local and global featuresrdquo Journal of Signal amp InformationProcessing vol 4 no 3 2013
[10] O Ludwig Jr and U Nunes ldquoImproving the generalizationproperties of neural networks an application to vehicle detec-tionrdquo in Proceedings of the 11th International IEEE Conferenceon Intelligent Transportation Systems (ITSC rsquo08) pp 310ndash315December 2008
[11] G E Hinton S Osindero and Y-W Teh ldquoA fast learningalgorithm for deep belief netsrdquoNeural Computation vol 18 no7 pp 1527ndash1554 2006
[12] V Nair and G E Hinton ldquo3D object recognition with deepbelief netsrdquo in Proceedings of the 23rd Annual Conference onNeural Information Processing Systems (NIPS rsquo09) pp 1339ndash1347December 2009
[13] G Taylor R Fergus Y L Cun and C Bregler ldquoConvolutionallearning of spatio-temporal featuresrdquo in Computer VisionmdashECCV 2010 vol 6316 of Lecture Notes in Computer Science pp140ndash153 2010
[14] C X Zhang J S Zhang N N Ji et al ldquoLearning ensemble clas-sifiers via restricted Boltzmann machinesrdquo Pattern RecognitionLetters vol 36 pp 161ndash170 2014
[15] S-H Zhong Y Liu and Y Liu ldquoBilinear deep learning forimage classificationrdquo in Proceedings of the 19th ACM Interna-tional Conference on Multimedia ACM Multimedia (SIG MMrsquo11) December 2011
[16] S YanD Xu B ZhangH-J ZhangQ Yang and S Lin ldquoGraphembedding and extensions a general framework for dimen-sionality reductionrdquo IEEE Transactions on Pattern Analysis andMachine Intelligence vol 29 no 1 pp 40ndash51 2007
[17] F Wood and G E Hinton ldquoTraining products of experts byminimizing contrastive divergencerdquo Tech Rep 2012 53 143Brown University 2012
Submit your manuscripts athttpwwwhindawicom
Computer Games Technology
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Distributed Sensor Networks
International Journal of
Advances in
FuzzySystems
Hindawi Publishing Corporationhttpwwwhindawicom
Volume 2014
International Journal of
ReconfigurableComputing
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Applied Computational Intelligence and Soft Computing
thinspAdvancesthinspinthinsp
Artificial Intelligence
HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014
Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Journal of
Computer Networks and Communications
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation
httpwwwhindawicom Volume 2014
Advances in
Multimedia
International Journal of
Biomedical Imaging
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ArtificialNeural Systems
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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