car3d recognition

3-D Model Based Vehicle Recognition

Jan Prokaj and Gerard MedioniInstitute for Robotics and Intelligent Systems

University of Southern CaliforniaLos Angeles, CA 90089

[email protected], [email protected]

Abstract

We present a method for recognizing a vehicles makeand model in a video clip taken from an arbitrary viewpoint.This is an improvement over existing methods which requirea front view. In addition, we present a Bayesian approachfor establishing accurate correspondences in multiple viewgeometry.

We take a model-based, top-down approach to classifyvehicles. First, the vehicle pose is estimated in every frameby calculating its 3-D motion on a plane using a structurefrom motion algorithm. Then, exemplars from a databaseof 3-D models are rotated to the same pose as the vehicle inthe video, and projected to the image. Features in the modelimages and the vehicle image are matched, and a modelmatching score is computed. The model with the best scoreis identified as the model of the vehicle in the video.

Results on real video sequences are presented.

1. Introduction

The number of surveillance systems around us has in-creased in recent years and is likely to continue growing.Thanks to advances in hardware and lower manufacturingcosts, video cameras in these systems now have very highresolution (1080i is commonly supported by cameras onstore shelves). This combination of more video sources andhigher resolutions produces a staggering amount of data,which must be then reviewed in some way. However, theamount of data precludes a thorough search for objects ofinterest by a human operator. Instead, content-based re-trieval algorithms need to be used to solve this problem.The question then becomes what can be inferred from theraw video data.

In this work, our interest is in surveillance systems wherethe objects of interest are vehicles. Figure 1 illustrates thesurveillance scenario considered in this paper. The mostcommon information gathered about vehicles from video is

Figure 1. The surveillance scenario considered here.

their position over time. There may be some additional in-formation such as the vehicles appearance, and broad cat-egory (sedan, van, truck), but little beyond that. A highlydesirable information that is currently not inferred is the ve-hicles make and model.

Knowing the make and model of a vehicle is desirable,because, among other things, it allows queries to a retrievalsystem such as When was the last time a Ford Focus trav-eled through this location?. The difficulties in achievingthis goal include varying appearance (due to body color andreflections), the varying pose of a vehicle, and the relativelyfine (small-scale) features distinguishing different vehiclemodels from each other.

The main contribution of this paper is a new algorithmthat solves exactly this problem: inferring the vehiclesmake and model in video clip taken from an arbitrary view-point. The key idea is to determine the vehicles pose, andmatch the projection of a 3-D model to the vehicle in thevideo.

Until recently, vehicle type recognition was limited toidentifying one of a small set of generic categories, such as asedan, or a truck [4, 3, 9, 5]. This limitation was removed byPetrovic and Cootes in [15], where the specific vehicle makeand model was identified. In that work, a particular regionof the car (the front) is used for recognition. This region

978-1-4244-5498-3/09/$25.00 2009 IEEE

Authorized licensed use limited to: University of Southern California. Downloaded on March 01,2010 at 13:52:09 EST from IEEE Xplore. Restrictions apply.

is normalized to a fixed size, and various features capturingthe image structure are calculated from it and form a fea-ture vector. Nearest neighbor classification is used to finallyidentify the specific vehicle type. Of the features tested, thebest performance (over 97% vehicles correctly identified)was achieved using Square Mapped gradients. In the samevein, Negri et al [14] used slightly different features, alsobased on gradients, and a different, voting-based, classifier,and achieved similar performance.

The weakness of these methods is their reliance on a spe-cific viewpoint, the vehicles front-view. In addition, thesemethods work on single images, and do not take advantageof multiple frame data available in a video surveillance con-text. In this work, we remove these restrictions, and showthat vehicle make and model can be identified from a videofrom an arbitrary viewpoint, and with a great accuracy.

We take a model-based, top-down approach. First, thevehicle pose is estimated in every frame by calculating its3-D motion using a structure from motion algorithm andassuming the vehicle is moving forward and on a plane.Then, each model in the database of 3-D models is rotatedto the same pose, and projected to the image. Features inthe model images and the vehicle image are matched, and amodel similarity score is computed. The model with thebest score is reported as the model of the vehicle in thevideo.

To establish correspondences for the structure from mo-tion algorithm, we take inspiration from [20, 2] and use aglobal motion pattern as a prior in calculating sparse opti-cal flow. This increases the tolerance of correspondences tonoise from reflections on the car and to ambiguities arisingfrom the aperture problem.

The reconstruction problem in our case is made easierby realizing the motion of the vehicle is planar. There areseveral approaches [17, 7, 11] to solving the structure frommotion problem with this useful constraint. In [17], Rothershows that knowledge of four coplanar points simplifies theproblem to a linear system of equations. A plane+parallaxformulation of the problem in [7] yields dense reconstruc-tion using direct image measurements without the need forimage correspondences. A factorization approach to solvethis problem is presented in [11]. Here, we solve this prob-lem by assuming a calibrated camera and a knowledge ofthe ground plane, and using an incremental reconstructionalgorithm. The camera is assumed to be static (or stabi-lized). The additional knowledge is used to constrain opti-mization of the reprojection error.

2. CorrespondencesAccurate correspondences are critical to the success of

algorithms taking advantage of multiple view geometry.Since we are dealing with video data, we would like to usean optical flow based algorithm to get a sparse set of cor-

respondences between adjacent frames of the video. Theproblem is that in our setting, the correspondences are onthe car body, which has a strong specular component. Thiscauses changes in appearance from frame to frame. In addi-tion, we need to deal with ambiguities caused by the aper-ture problem. To handle all these problems, we formulatethe optical flow problem in a Bayesian setting, and use theglobal motion pattern as a prior. The effect of this prior isto guide the search for correspondences. This idea has beensuccessfully used in tracking vehicles in airborne video andtracking in high density crowd scenes [20, 2].

This global motion pattern is calculated by tracking thevehicle in the video. Here we use a simple tracker basedon the Hungarian algorithm [8]. We first subtract the back-ground to find vehicles in every frame (assuming the onlylarge moving objects in the scene are vehicles). The back-ground is modeled as the mode of a sliding window offrames. Then, vehicles are associated from frame to frameusing the Hungarian algorithm, where the similarity be-tween two vehicles in different frames is based on howmuch they overlap. The frame rate is high enough, suchthat the motion smoothness assumption is satisfied. Thefinal trajectory of a vehicle is then used as a prior in thefollowing optical flow computation.

The optical flow is calculated for a sparse set of stablefeatures, which are computed using the Harris corner de-tector. For each feature i, the probability of flow vi in theframe Ij is

p(vi|Ij) p(Ij |vi)p(vi) (1)where p(Ij |vi) denotes the image likelihood and p(vi) isthe flow prior. The flow is then the maximum a posteriori(MAP) estimate,

vi = argmaxv

p(v|Ij) (2)If the probability of vi is less than a threshold, this featurepoint is removed.

Here, vi is a discrete variable, whose possible values aredetermined by a small circular region centered on the pre-dicted location of the feature point in the current frame. Theradius of the region is 1.5v, where v is the global motionin the current frame computed from the vehicle trajectory.If the position of the vehicles center in the current frame iscj , and in the previous frame is cj1,

v = cj cj1 (3)The prediction location of the feature point, xi, is simplythe previous position, xi1 plus v.

The flow prior regularizes both the flow direction andmagnitude:

p(vi) = pdirection(vi)pmagnitude(vi) (4)pdirection(vi) = k 0.5(vi v + 1) (5)pmagnitude(vi) = k e(viv)

2/2 (6)


where k denotes a normalization constant so that the proba-bility of all possible flows sums to 1, and is set to a fixedvalue, or a fraction of v, such that the allowable rangeof flow magnitude has non-zero probability. The prior ex-presses that the flow should be similar in magnitude anddirection to the global motion of the vehicle.

The flexibility of a Bayesian formulation allows one tomodel the likelihood in many different ways. Here, thelikelihood is computed using normalized cross-correlationat multiple scales:

p(Ij |vi) =sS

ksNCC(Ij1(xi1 + vi), Ij(xi), s) (7)

where S denotes a small set of square sized windows, andk is again a normalization constant. Cross-correlation ischosen for its invariance to lighting changes.

In the current formulation, vi is integer valued. Sincewe need accurate correspondences for geometry calcula-tions, subpixel accuracy is desired. To accomplish this, aquadratic is fit to p(v|I) in the immediate neighborhood ofthe optimum vi .

If the vehicle trajectory is noisy, it can adversely impactthe flow estimate through the influence of the prior. Thisproblem is handled by simultaneously estimating v in anEM-like fashion over a small number of iterations. First,(2) is solved using the current estimate of v. Then v iscomputed as

v = E[v] =1N

i

vi (8)

This effectively removes any noise in v in as few as 3 itera-tions.

If the likelihood only considers translating motion, as inour case by using cross-correlation, some accuracy is lost.This can be rectified by realizing that the motion of the cor-respondences is planar. So to further refine the estimates ahomography is estimated between the frames, and the pro-cess just described repeated, only this time with one of theframes warped in the cross-correlation (likelihood) compu-tation.

The complexity of this approach is dominated by thelikelihood computation. The likelihood needs to be deter-mined for every possible value of flow, which can be a rel-atively large set of values to consider. In our implementa-tion, we used normalized cross-correlation at three differentscales to compute the likelihood, which is inefficient, butmore practical schemes can be easily substituted.

3. Structure from MotionThe needed pose of the vehicle is determined by calcu-

lating its 3-D motion on a plane and assuming the vehicle ismoving forward. In our current implementation the camera

is assumed to be static, but a moving camera can be incorpo-rated by stabilizing the video first. Since we are interestedin the pose of the moving vehicle, only correspondences onthe vehicle are used. These are determined from the back-ground subtraction. A standard pinhole camera model isassumed, and the camera is calibrated off-line, which is rea-sonable in a surveillance context.

Determining the motion of the vehicle with a static cam-era is equivalent to assuming the vehicle is static and deter-mining the motion of a virtual camera. We now show thatthe motion of a virtual camera is inverse of the motion of thevehicle. Since a vehicle is a rigid body, its motion is com-pletely described by a 3-D rotation, Rm, and a translation,tm:

M =[Rm tm0T 1

](9)

Let xi = PstaticMX be the projection of an arbitrarypointX on the moving vehicle with a static cameraPstatic.Then,

xi = PstaticMX= K[R RC]MX= K[RRm (Rtm RC)]X= K[RRm R(C tm)]X= K[RRm RRm(R1m (C tm))]X= K[R RC]X

where the motion of a virtual camera from C to its newposition, C, is exactly the inverse of the motion of the ve-hicle.

The key property enforced in our structure from motionalgorithm is that the motion of the virtual camera is in aplane parallel to the ground plane. Therefore, we need toknow the ground plane. This can be calculated automat-ically from vanishing points, or determined interactively[19, 6]. Since this only needs to be done once, a semi-automatic method is acceptable. The knowledge of theground plane is used to define a world coordinate system(WCS) where the x and y axes span the plane, the z axis isperpendicular to the plane, pointing up, and the origin is setby choosing an arbitrary point on the plane. This is illus-trated in Figure 2 (a left-handed coordinate system is used).Defining the WCS this way makes it easy to constrain themotion as desired.

With the WCS determined, we can define the virtualcamera projection matrix in the first frame, which is thesame as that of the static camera. The camera matrices inthe following frames will be determined with respect to thiscamera. Using the notation in [11], let gx,gy,gz be theaxes of the WCS in the camera coordinate system (CCS),and let g0 be the origin in CCS. If (x, y) is the location ofthe origin in the image, then g0 = (x, y, f)T , where f


Figure 2. The chosen world coordinate system.

is the known focal length, and is the perspective depth,which is set arbitrarily. The camera projection matrix isthen

P0 = K[gx gy gz g0

]= K

[R0 g0

]where K is the known camera calibration matrix.

An incremental structure from motion algorithm similarto [16] is used. The video is temporally downsampled toprovide a more stable wider baseline in the geometry cal-culations. In the first two frames, an essential matrix isestimated. RANSAC is used to remove errors in correspon-dences. The pose of the second camera relative to the first isdetermined by decomposing the essential matrix and choos-ing one of four possible solutions [6]. Letting the first cam-era be P0, and the relative pose be [R1 t1], the absolutepose of the second camera is

[R0R1 (R0t1 + g0)] (10)

This absolute pose of the second camera is optimized us-ing Levenberg-Marquardt [12] by minimizing the repro-jection error such that the motion of the camera is in theground plane. That is, the rotation is constrained to be about(0, 0, 1), the ground plane normal, and the center of projec-tion is constrained to lie in the same plane as P0, which isparallel to the ground plane. In total, there are four degreesof freedom. Triangulation in two views is done optimallyby solving a 6th degree polynomial, which represents thereprojection error parameterized by the choice of epipolarlines [6].

In subsequent frames, the camera pose is estimated bysolving the Perspective-n-Point problem. We use the re-cent algorithm by Lepetit et al [10], which is fast, non-iterative, and gives very accurate results. Additional robust-ness against noise is gained by using RANSAC. Triangula-tion of points visible in multiple views is done by forminga linear system and solving with iteratively reweighted leastsquares.

After every frame is processed, bundle adjustment [18] isused to refine the current solution. The parameters are thecamera center (3 unknowns for each camera, except P0),the camera rotation about the ground plane normal (1 un-known for each camera, except P0), and point coordinates(3 unknowns for each point). As before, the cameras areconstrained to move in the same plane as P0. Levenberg-Marquardt [12] is used again to do the minimization of thereprojection error.

When the structure from motion algorithm completes,the vehicles motion direction (and thus pose) in frame iis estimated as (Ci Ci1), where Ci is the virtualcameras center of projection in frame i.

The complexity of this step is dominated by bundle ad-justment, but overall it is efficient. With the pose of thecameras constrained, the number of degrees of freedom isreduced, so our formulation is actually more efficient thanthe general case. Performance can be further improved bylimiting the bundle adjustment to cameras and structure inthe last n frames.

4. Model ClassificationThe knowledge of the vehicle pose and the pose of the

camera allows us to reduce the model classification prob-lem from 3-D to 2-D. This is important, because it makesthe problem easier, and the solution more reliable. Measur-ing similarity in a lower dimensional space is always easierthan in a higher-dimensional one. In addition, model classi-fication in 3-D would either require a dense reconstructionof the vehicle in the video, or a fit of sparse reconstructionto a model. Either option adds significant complexity, withno guaranteed gain in performance. Furthermore, the solu-tion for vehicle motion is easily constrained, as we have justshown, whereas the solution for vehicle structure can not be(easily) constrained at all. In other words, there is higherconfidence in the motion estimate than in the structure esti-mate.

The problem is reduced to 2-D by rotating a potential3-D vehicle model to the same pose as the vehicle in thevideo, and projecting it to the image. To perform classifi-cation, models from a database are projected in turn, andthe model with the best matching score is selected. Thisapproach depends on having access to a database of goodquality 3-D vehicle models, but this has become less of aproblem with the introduction of 3-D model sharing sites,such as Googles 3D Warehouse [1].

We would also like to point out that discretizing the ve-hicle pose space and skipping the pose estimation will notachieve the same result, unless the video capture is severelyrestricted. For example, our method will also work witha moving camera after adding a video stabilization mod-ule. But there is great benefit even in the static cameracase; when the vehicle in the video is making a turn, our


method can take advantage of seeing the vehicle from mul-tiple viewpoints.

The previous work in vehicle classification, and objectrecognition in general, has made good use of histogramsof oriented gradients as features. These seem to be verystable, and have good discriminatory characteristics, espe-cially when they are collected over multiple scales. Wemake use of this type of features here as well. The fea-tures are calculated the same way as SIFT [13] features, butthe rotation invariance of the descriptor is deliberately dis-abled. The vehicles in the model image and the video imageare already normalized to the same view, so enabling rota-tion invariance is actually detrimental. In addition, for thesame reason, when the features are matched between im-ages (as discussed below), the matches are restricted to bein similar scales. If the scale of a feature in the video imageis s, the scale, s, of a matching feature in the model imageneeds to be 0.75s < s < 1.5s.

Our strategy for measuring the similarity between an im-age of a vehicle from a video and the image of a vehiclefrom a model consists of two steps. First, we find featurecorrespondences in the images, and then we compare therelative positioning of the matched features in the model im-age with the relative positioning of the corresponding fea-tures in the video image. Feature correspondences are foundby finding the closest descriptor (measured with Euclideandistance) in the model image to each descriptor in the videoimage. If the distance to the closest descriptor is less thana threshold, the match is valid. In addition, to further de-crease the number of spurious matches, we only considerfeatures found at scales larger than some minimum scale(for example, by ignoring features found in the first octaveof the Laplacian pyramid). The reason is that features foundat small scales miss the big picture and their correspon-dences are not optimal in the global sense. Figure 3 showsan example of valid feature matches between the video im-age and the model image.

Let Sf = {(xi1,xi2)|i [1, N ]} be the set of valid fea-ture matches in frame f , where x = (x, y) and the sub-script indicates the model/video image, vij1 = x

i1xj1, and

vij2 = xi2 xj2. The video-model similarity in frame f is

simf (Sf ) =( |Sf |M

) 2N(N 1)

Ni=1

Nj=i+1

DirijMagij

Dirij = 0.5(vij1 vij2 + 1)

Magij = exp(|vij1 vij2 |)where M is the total number of features in the video image.The total video-model similarity is the average of all framesimilarities:

sim(S) =1F

Ff=1

simf (Sf ) (11)

Figure 3. Example feature matches.

where F is the total number of frames in the video. Thevehicle in the video is classified with the model having themaximum similarity.

The video-model similarity is efficient to compute.However, the vehicle classification speed decreases with alarger model database, because every model needs to beconsidered. To achieve a practical runtime performancewith large databases, we propose to use a hierarchical or-ganization of the models, where dissimilar models can bequickly identified, and skipped. This is the subject of ourfuture work.

5. ResultsVehicle recognition was evaluated on 20 video clips and

a database of 36 models. The length of videos ranged from4 to 40 frames. The viewpoints of vehicles in the videoscan be described as mostly front and mostly back. Thevideo resolution was 1920x1080. 3-D vehicle models weredownloaded from [1], and their complexity ranged from5,000 to 120,000 polygons. A subset of the models usedin the experiments is shown in Figure 4. Camera calibrationwas performed using Jean-Yves Bouguets camera calibra-tion toolbox. The ground plane was estimated using vanish-ing points and verified interactively.

Vehicle recognition performance is shown in Table 1.The first row indicates the performance of the algorithm inits standard configuration, where all frames of the video areused to determine the vehicle-model similarity. The nexttwo rows show the performance broken down by the gen-


Figure 4. A subset of the models used in the experiments.

Correct Classification (%)Rank 1 Rank 3

Multiple Frames 50 85- Front view (8/20) 62.5 87.5- Back view (12/20) 41.6 91.6Single Frame 15 40

Table 1. Vehicle recognition performance.

eral vehicle viewpoint. A rank-k classification means a ve-hicle is classified correctly if it is ranked k in the resultset. The result set is ordered using equation (11). Exam-ples of correct and incorrect vehicle recognition are shownin Figures 5 and 6.

The results show that our algorithm is excellent at iden-tifying the most likely makes and models for vehicles invideo. The correct make and model is always top ranked.This kind of performance lays very good ground work for asecond-stage fingerprinting algorithm, which can use veryfine features to determine the precise make and model for avehicle. The results also show that vehicles with a visiblefront were classified more accurately than vehicles with avisible back.

In addition to evaluating the algorithm in its standardconfiguration, we also evaluated it in single-frame mode,where only one frame of the video-clip is used in calculat-ing the vehicle-model similarity. The purpose of this ex-periment was to see whether classification performance im-proves with taking advantage of multiple frame data.

Clearly, the multiple-frame performance is much betterthan single-frame performance. This is not surprising sinceeach frame provides additional matching information to thealgorithm. The noise in feature matches is more tolerablewhen more frames are available.

6. ConclusionsWe presented a method for recognizing a vehicles make

and model in video clip taken from an arbitrary viewpoint.

The video-model similarity is determined by first match-ing features similar to histograms of oriented gradients andmeasuring the relative configuration of the features in videoand in the model. The results show excellent performanceat identifying the most likely vehicle make and model. Inaddition, we confirmed that higher classification accuracy isobtained when the front of the vehicle is visible.

We also presented a Bayesian approach for establish-ing accurate correspondences in multiple view geometry. Aglobal motion pattern was used as a prior in this approach.A structure from motion algorithm was then able to suc-cessfully determine the vehicle pose from these correspon-dences. In determining the vehicle pose, the key is to usethe knowledge of the ground plane to impose constraints onthe virtual camera motion.

The algorithms scalability to large model databases iscurrently being investigated. We propose to use a hierarchi-cal organization of the models, where dissimilar models canbe quickly identified, and skipped in subsequent computa-tion.

7. Acknowledgments

This work was supported in part by grant DE-FG52-08NA28775 from the U.S. Department of Energy.

References[1] Google 3D warehouse. http://sketchup.google.com/3dwarehouse/.[2] S. Ali and M. Shah. Floor fields for tracking in high density

crowd scenes. In 10th European Conference in ComputerVision, volume 5303 of LNCS, pages 114. Springer Berlin /Heidelberg, 2008.

[3] M.-P. Dubuisson Jolly, S. Lakshmanan, and A. Jain. Vehiclesegmentation and classification using deformable templates.IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 18(3):293308, Mar 1996.

[4] J. M. Ferryman, A. D. Worrall, G. D. Sullivan, and K. D.Baker. A generic deformable model for vehicle recognition.


Figure 5. Examples of correct vehicle recognition.

Figure 6. Examples of incorrect vehicle recognition.

In British conference on Machine vision, volume 1, pages127136. BMVA Press, 1995.

[5] D. Han, M. Leotta, D. Cooper, and J. Mundy. Vehicle classrecognition from video-based on 3d curve probes. In 2ndJoint IEEE International Workshop on Visual Surveillanceand Performance Evaluation of Tracking and Surveillance,pages 285292, Oct. 2005.

[6] R. Hartley and A. Zisserman. Multiple View Geometry inComputer Vision. Cambridge University Press, 2nd edition,2003.

[7] M. Irani, P. Anandan, and M. Cohen. Direct recovery ofplanar-parallax from multiple frames. IEEE Transactionson Pattern Analysis and Machine Intelligence, 24(11):15281534, Nov 2002.

[8] H. Kuhn. The hungarian method for the assignment problem.Naval Research Logistics Quarterly, 2(1-2):8397, March1955.

[9] A. Lai, G. Fung, and N. Yung. Vehicle type classificationfrom visual-based dimension estimation. In IEEE IntelligentTransportation Systems, pages 201206, 2001.

[10] V. Lepetit, F. Moreno-Noguer, and P. Fua. Epnp: An accurateo(n) solution to the pnp problem. International Journal ofComputer Vision, 81(2):155166, 2009.

[11] J. Li and R. Chellappa. A factorization method for structurefrom planar motion. In IEEE Workshop on Motion and VideoComputing, ACV/MOTIONS 05, volume 2, pages 154159,Jan. 2005.

[12] M. Lourakis. levmar: Levenberg-marquardtnonlinear least squares algorithms in C/C++.http://www.ics.forth.gr/ lourakis/levmar/, 2009.

[13] D. G. Lowe. Distinctive image features from scale-invariantkeypoints. International Journal of Computer Vision,60(2):91110, 2004.

[14] P. Negri, X. Clady, M. Milgram, and R. Poulenard. Anoriented-contour point based voting algorithm for vehicletype classification. In 18th International Conference on Pat-tern Recognition, volume 1, pages 574577, 2006.

[15] V. Petrovic and T. F. Cootes. Analysis of features for rigidstructure vehicle type recognition. In British Machine VisionConference, pages 587596, 2004.

[16] M. Pollefeys, L. Van Gool, M. Vergauwen, F. Verbiest,K. Cornelis, J. Tops, and R. Koch. Visual modeling witha hand-held camera. International Journal of Computer Vi-sion, 59(3):207232, 2004.

[17] C. Rother and S. Carlsson. Linear multi view reconstructionand camera recovery. In IEEE International Conference onComputer Vision, volume 1, pages 4250 vol.1, 2001.

[18] B. Triggs, P. McLauchlan, R. Hartley, and A. Fitzgibbon.Vision Algorithms: Theory and Practice, volume 1883 ofLNCS, chapter Bundle Adjustment A Modern Synthesis,pages 153177. Springer Berlin / Heidelberg, 2000.

[19] A. D. Worrall, G. D. Sullivan, and K. D. Baker. A simple,intuitive camera calibration tool for natural images. In Con-ference on British machine vision, volume 2, pages 781790.BMVA Press, 1994.

[20] Q. Yu and G. Medioni. Motion pattern interpretation anddetection for tracking moving vehicles in airborne videos. InIEEE Computer Society Conference on Computer Vision andPattern Recognition, pages 26712678, 2009.


car3d recognition

Documents