Research ArticleLearning a Mid-Level Representation forMultiview Action Recognition

Cuiwei Liu ,1 Zhaokui Li,1 Xiangbin Shi ,1,2 and Chong Du 3

1School of Computer Science, Shenyang Aerospace University, Shenyang City, Liaoning Province 110136, China2School of Information, Liaoning University, Shenyang City, Liaoning Province 110136, China3Shenyang Aircraft Design and Research Institute, Tawan Street 40, Shenyang City, Liaoning Province 110135, China

Correspondence should be addressed to Cuiwei Liu; liucuiwei@sau.edu.cn

Received 13 November 2017; Revised 12 February 2018; Accepted 15 February 2018; Published 20 March 2018

Academic Editor: Martin Reisslein

Copyright © 2018 Cuiwei Liu et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Recognizing human actions in videos is an active topic with broad commercial potentials. Most of the existing action recognitionmethods are supposed to have the same camera view during both training and testing. And thus performances of these single-viewapproaches may be severely influenced by the camera movement and variation of viewpoints. In this paper, we address the aboveproblem by utilizing videos simultaneously recorded from multiple views. To this end, we propose a learning framework basedon multitask random forest to exploit a discriminative mid-level representation for videos from multiple cameras. In the first step,subvolumes of continuous human-centered figures are extracted from original videos. In the next step, spatiotemporal cuboidssampled from these subvolumes are characterized by multiple low-level descriptors. Then a set of multitask random forests arebuilt upon multiview cuboids sampled at adjacent positions and construct an integrated mid-level representation for multiviewsubvolumes of one action. Finally, a random forest classifier is employed to predict the action category in terms of the learnedrepresentation. Experiments conducted on the multiview IXMAS action dataset illustrate that the proposed method can effectivelyrecognize human actions depicted in multiview videos.

1. Introduction

Automatic recognition of human actions in videos becomesincreasingly important in many applications such as intelli-gent video surveillance, smart home system, video annota-tion, and human-computer interaction. For example, findingout suspicious human behaviors in time is an essential task inintelligent video surveillance, and identifying fall actions ofolder people is of great importance for a smart home system.In recent years, a variety of action recognition approaches[1–5] have been proposed to solve single-view tasks, andsome surveys [6–10] review the advances of single-viewaction recognition in detail. However, real-world videos bringabout great challenges to single-view action recognition,since visual appearance of actions can be severely affected byviewpoint changes and self-occlusion.

Different from single-view approaches which utilize onecamera to capture human actions, multiview action recog-nition methods exploit several cameras to record actionsfrom multiple views and try to recognize actions by fusing

multiview videos. One strategy is to handle the problem ofmultiview action recognition at classification level by anno-tating videos from multiple views separately and mergingthe predicted labels of all views. Pehlivan and Forsyth [11]designed a fusion scheme of videos from multiple views.They firstly annotated labels over frames and cameras usinga nearest neighbor query technique and then employed aweighting scheme to fuse action judgments as the sequencelabel. Another group of methods resort to merging data frommultiple views at feature level. These methods [12–15] utilize3D or 2D models to build a discriminative representation ofan action based on videos from multiple views. In fact, howto represent an action video with expressive features playsan especially important role in both multiview and single-view action recognition. A video representation with strongdiscriminative anddescriptive ability is able to express humanaction reasonably and supply sufficient information to actionclassifier, which will lead to an improvement in recognitionperformance.

HindawiAdvances in MultimediaVolume 2018, Article ID 3508350, 10 pageshttps://doi.org/10.1155/2018/3508350

2 Advances in Multimedia

This paper presents a multiview action recognition ap-proach with a novel mid-level action representation. A learn-ing framework based on multitask random forest is pro-posed to exploit a discriminative mid-level representationfrom low-level descriptors of multiview videos. The inputof our method is multiview subvolumes, each of whichincludes continuous human-centered figures. These subvol-umes simultaneously record one action from different per-spectives. And then we sample spatiotemporal cuboids fromsubvolumes at regular positions and extract multiple low-level descriptors to characterize each cuboid. During train-ing, cuboids from multiple views sampled at four adjacentpositions are grouped together to construct a multitask ran-dom forest by using action category and position as two re-lated tasks, and a set of multitask random forests are con-structed in this way. In testing, each cuboid is classified bythe corresponding random forest, and a fusion strategy isemployed to create an integrated histogram for describingcuboids sampled at a certain position of multiview subvol-umes. Histograms of different positions are concatenated toa mid-level representation for subvolumes simultaneouslyrecorded from multiple views. Moreover, the integratedhistogram of multiview cuboids is created in terms of thedistributions of both action categories and cuboid positions,which endows the learned mid-level representation with theability of exploiting spatial context of cuboids. To achievemultiview action recognition, a random forest classifier isadopted to predict the category of this action. Figure 1 depictsthe overview of our multitask random forest learning frame-work.

The remainder of this paper is organized as follows. Aftera brief overview of the related work in Section 2, we detailedlydescribe our method in Sections 3, 4, and 5. Then a descrip-tion of experimental evaluation procedure followed by theanalysis of results is given in Section 6. Finally, the paperconcludes with discussions and conclusions in Section 7.

2. Related Work

The existing multiview action recognition methods fusingdata at feature level can be roughly categorized into twogroups, 3D based approaches and 2D based approaches.

Some action recognition methods based on 3D modelshave shown good performance on several public multiviewaction datasets. Weinland et al. [12] built 3D action represen-tations based on invariant Fourier analysis of motion historyvolumes by using multiple view reconstructions. For thepurpose of considering viewdependency among cameras andadding full flexibility in camera configurations, they designedan exemplar-based hidden Markov model to characterizeactions with 3D occupancy grids constructed from multipleviews in another work [16]. Holte et al. [14] combined 3Doptical flow of each view into enhanced 3D motion vectorfields, which are described with the 3D Motion Context andthe view-invariant Harmonic Motion Context in a view-invariant manner. Generally, 3D reconstruction from mul-tiple cameras requires additional processing such as cameracalibration, which would lead to high computational cost andreduce the flexibility. In order to overcome the limitation of

3D reconstruction from 2D images, some methods employdepth sensors for multiview action recognition. Hsu et al.[17] addressed the problem of view changes by using RGB-Dcameras such as Microsoft Kinect. They constructed a view-invariant representation based on the Spatiotemporal Matrixand integrated the depth information into the spatiotemporalfeature to improve the performance.

In recent years, different methods based on 2D modelshave been proposed for multiview action recognition. Thesemethods aim to construct discriminative and view-invariantaction representations from one or more descriptors. Sou-venir and Babbs [13] learned low-dimensional and view-independent representations of actions recorded frommulti-ple views by usingmanifold learning. In thework of [18], scaleand location invariant features are calculated from humansilhouettes to obtain sequences of multiview key poses, andaction recognition is achieved throughDynamic TimeWarp-ing. Kushwaha et al. [19] extracted scale invariant con-tour-based pose features and uniform rotation invariantlocal binary patterns for view-invariant action recognition.Sargano et al. [20] learned discriminative and view-invariantdescriptors for real-time multiview action recognition byusing region-based geometrical and Hu-moments featuresextracted fromhuman silhouettes. Chun and Lee [15] extract-ed local flow motion from multiview image sequences andestimated the dominant angle and intensity of optical flow forhead direction identification. Then they utilized histogramof the dominant angle and intensity to represent each se-quence and concatenated histograms of all views as the finalfeature of multiview sequences. Murtaza et al. [21] devel-oped a silhouette-based view-independent action recognitionscheme. They computed Motion History Images (MHI) foreach view and employed Histograms of Oriented Gradients(HOG) to extract low-dimensional description of them. Gaoet al. [22] evaluated seven popular regularized multitasklearning algorithms on multiview action datasets and treateddifferent actions as different tasks. In their work, videos fromeach view are handled separately. Hao et al. [23] employed asparse coding algorithm to transfer the low-level features ofmultiple views into a discriminative and high-level semanticsspace and achieved action recognition by amultitask learningapproach in which each action is considered as an individualtask.

Besides, some other methods employ deep learning tech-nique to learn discriminative features for multiview actionrecognition, and several neural networks are developed tobuild these deep-learned features directly from the raw data.Lei et al. [24] utilized convolutional neural network to extracteffective and robust action features for continuous actionsegmentation and recognition under multiview setup.

The proposed method is also relevant to our previouswork [25], in which a random forest based learning frame-work is designed for building mid-level representations ofaction videos. Different from [25], the proposedmethod aimsto solve the problem of multiview action recognition, and anintegrated mid-level representation is learned for an actiondepicted in videos recorded frommultiple views. Meanwhile,our multitask random forest learning framework is able toeffectively exploit the spatial context of cuboids.

Advances in Multimedia 3

Histograms

Classifier

Action label: sit down

Multitask random forests

View M

Cuboids Subvolumes

View 1

x1 x2 x3 x4

x5 x6 x7 x8

x9 x10 x11 x12

x13 x14 x15 x16

x17 x18 x19 x20

x21 x22 x23 x24

MTRF 6MTRF 1MTRF 1 MTRF 2

MTRF 3 MTRF 4

MTRF 5 MTRF 6

x1 x2 x3 x4

x5 x6 x7 x8

x9 x10 x11 x12

x13 x14 x15 x16

x18x17 x19 x20

x21 x22 x23 x24

MTRF 1 MTRF 2

MTRF 3 MTRF 4

MTRF 5 MTRF 6

MTRF 6MTRF 1

... ...

......

· · · · · ·

· · ·· · ·

· · ·

· · ·

· · ·

Figure 1: Framework of ourmethod. Firstly, we densely sample spatiotemporal cuboids from subvolumes of𝑀 views. Suppose that 24 cuboidsare extracted from one subvolume, and then six multitask random forests are constructed, each of which is built upon cuboids frommultipleviews sampled at four adjacent positions. Then all cuboids are classified by their corresponding random forests, and an integrated histogramis created to represent cuboids of all the𝑀 views sampled at the same position. The concatenation of histograms for all positions constitutesa mid-level representation of the input𝑀 subvolumes. At last, random forest is utilized as the final action classifier.

3. Overview of Our Method

Our goal is to recognize a human action by using videos re-corded from multiple views. To this end, we propose a novelmultitask random forest framework to learn a uniform mid-level feature for an action. In order to remove the influence ofthe background, we firstly employ a human body detector ortracker to obtain the human-centered figures from a video,and then a video is divided to a series of subvolumes withfixed size, each of which is a sequence of human-centeredfigures.We densely extract spatiotemporal cuboids (e.g., 15 ×15 × 10) from subvolumes, and each of them is representedby multiple low-level features.

Our multitask random forest framework utilizes a fusionstrategy to get an integrated histogram feature for cuboidssampled at the same position of subvolumes that simulta-neously record an action from different views. Concretely, amultitask random forest is built upon cuboids extracted atfour adjacent positions ofmultiview subvolumes, and thus wecan construct a set ofmultitask random forests correspondingto different groups of positions. For the purpose of exploitingspatial context of cuboids, position of cuboid is treated asanother task besides action category in the constructionof multitask random forest. Decision trees in a multitaskrandom forest vote on the action category and position ofcuboids and generate a single histogram for cuboids sampledat the same position of simultaneously recorded multiviewsubvolumes, according to the distribution of both action cate-gory and cuboid position.The concatenation of histograms ofall positions is normalized to get themid-level representationfor multiview subvolumes. For multiview action recognition,

a random forest classifier is adopted to predict the category ofthis action.

4. Low-Level Features

Our multitask random forest based framework is general formerging multiple low-level features. In our implementation,we extract three complementary low-level features to de-scribe the motion, appearance, and temporal context of theinterested human. The optical flow feature computed fromthe entire human figure is able to characterize global mo-tion information, the HOG3D spatial-temporal descriptorextracted from a single cuboid captures the local motion andappearance information, and the temporal context featureexpresses the relative temporal location of cuboids.Therefore,the mid-level feature built upon the above three types oflow-level features is more robust to video variations such asglobal deformation, local partial occlusion, and diversity ofmovement speed.

4.1. Optical Flow. Optical flow [35] is used to calculate themotion between two adjacent image frames. This motiondescriptor shows favorable performance with noise, so itcan tolerate the jitter of human figures caused by humandetector or tracker. Given a sequence of human-centeredfigures, pixel-wise optical flow feature is calculated at eachframe using Lucas-Kanade algorithm [36]. The optical flowvector field 𝐹 is split into two scalar fields corresponding tothe horizontal and vertical components of the flow,𝐹𝑥 and𝐹𝑦.Then 𝐹𝑥 and 𝐹𝑦 are half-wave rectified into two nonnegative

4 Advances in Multimedia

channels 𝐹+𝑥 , 𝐹−𝑥 and 𝐹+𝑦 , 𝐹−𝑦 , respectively; namely, 𝐹𝑥 =𝐹+𝑥 + 𝐹−𝑥 and 𝐹𝑦 = 𝐹+𝑦 + 𝐹−𝑦 . Each channel is blurred withGaussian filter and normalized to obtain the final four sparseand nonnegative channels, 𝐹𝑏+𝑥 , 𝐹𝑏−𝑥 , 𝐹𝑏+𝑦 , and 𝐹𝑏−𝑦 , whichconstitute the motion descriptor of each frame.

4.2. HOG3D. HOG3D [37] is a local spatiotemporal descrip-tor based on histograms of oriented 3D spatiotemporalgradients. It is an extension of HOG descriptor [38] to thevideo. 3D gradients are calculated with arbitrary spatial andtemporal scales, followed by the orientation quantizationusing regular polyhedrons. A local support region is dividedinto𝐶1×𝐶1×𝐶2 cells. An orientation histogram is computedfor each cell, and the concatenation of all histograms isnormalized to generate the final descriptor. In this paper,HOG3D descriptors are computed for cuboids densely sam-pled from human-centered subvolumes. We set 𝐶1 = 4and 𝐶2 = 3, respectively, and utilize icosahedron with fullorientation to quantize the 3D gradients of each cell. So thedimension of HOG3D feature is 4 × 4 × 3 × 20 = 960.

4.3. Temporal Context. Temporal context feature is character-ized by the temporal relation among different cuboids and isregarded as a type of low-level feature in this paper. Given avideowith𝐿 frames, a cuboid𝑥 is extracted froma subvolumewhich contains 𝐿0 frames and begins with the 𝑙th frame.The temporal context of 𝑥 is described as a two-dimensionalvector [𝑙/𝐿, |𝑙/𝐿| − 0.5]𝑇, where 𝑙/𝐿 represents the temporalposition of 𝑥 in the whole video and |𝑙/𝐿 − 0.5| denotes thetemporal offset of 𝑥 relative to the center of the video.

5. Multitask Random ForestLearning Framework

We detail the proposed multitask random forest frameworkin this section. Suppose that an action is recorded by 𝑀cameras simultaneously, and we obtain 𝑉 human-centeredsubvolumes with fixed size from each video, denoted as{vol𝑚,V}𝑚=1:𝑀,V=1:𝑉.Thenwe densely sample𝐾 spatiotemporalcuboids from every subvolume with particular size and strideand denote them by {𝑥𝑚,V

𝑘}𝑘=1:𝐾,𝑚=1:𝑀,V=1:𝑉, each of which

is characterized by multiple low-level features. In order toexploit the spatial context of cuboids, we treat spatial positionof cuboids as another type of annotations and employ cuboidsof various action instances extracted at adjacent positions tobuild a multitask random forest by using both action labelsand position labels. The proposed multitask random forestframework constructs an integrated histogram to describecuboids {𝑥𝑚,V

𝑘}𝑚=1:𝑀 sampled at position 𝑘 of multiview

subvolumes {vol𝑚,V}𝑚=1:𝑀, and histograms of 𝐾 cuboids areconcatenated to create a unified mid-level representation forsubvolumes {vol𝑚,V}𝑚=1:𝑀 that are simultaneously recordedfrom multiple views.

5.1. Construction of Multitask Random Forest. Our train-ing cuboids {𝑥𝑚,V

𝑘,𝑖, 𝑦𝑖}𝑘=1:𝐾,𝑚=1:𝑀,V=1:𝑉𝑖 ,𝑖=1:𝑁 are extracted from

subvolumes of 𝑁 action instances, and each video of the 𝑖th

instance generates𝑉𝑖 subvolumes. Cuboids of the 𝑖th instanceshare the same action label 𝑦𝑖, and the position label ofcuboid 𝑥𝑚,V

𝑘,𝑖is 𝑘. As is shown in Figure 1, we draw cuboids at

regular positions of subvolumes and utilize training cuboidssampled at four adjacent positions to construct a multitaskrandom forest. We totally obtain 𝑅 random forests, denotedas {MTRF𝑟}𝑟=1:𝑅.

Multitask random forest MTRF𝑟 = {Tree𝑚𝑟,𝑡}𝑚=1:𝑀,𝑡=1:𝑇is an ensemble of 𝑀 ∗ 𝑇 decision trees, and each tree onlytakes cuboids from a particular view as input. Decision trees{Tree𝑚𝑟,𝑡}𝑡=1:𝑇 of the same view share the original training set𝐷𝑚𝑟 = {𝑥𝑚,V

𝑘,𝑖, 𝑦𝑖}𝑘∈cub(𝑟),V=1:𝑉𝑖 ,𝑖=1:𝑁, where cub(𝑟) represents the

set of positions belonging tomultitask random forestMTRF𝑟.As is shown in Figure 1, cub(𝑟) includes four positions thatare adjacent in the direction of width or height. For example,cuboids at position 1, position 2, position 5, and position 6(i.e., 𝑥1, 𝑥2, 𝑥5, and 𝑥6 in Figure 1) belong to MTRF1, andthus cub(1) = {1, 2, 5, 6}.

In order to build decision tree Tree𝑚𝑟,𝑡, we randomlysample about 2/3 of cuboids from the original training set𝐷𝑚𝑟 and obtain its own training dataset 𝐷𝑚𝑟,𝑡, using bootstrapmethod. All of the training cuboids in 𝐷𝑚𝑟,𝑡 go through thetree from the root. We split a node and the training cuboidsassigned to it according to a particular feature chosen froma set of randomly sampled feature candidates. Since a cuboidis described by three types of low-level features (i.e., opticalflow, HOG3D, and temporal context), two parameters 𝛾 ∈(0, 1) and 𝜏 ∈ (0, 1) are predefined to control the selectionof feature candidates. Specifically, we generate two randomnumbers 𝜉 ∈ [0, 1] and 𝜃 ∈ [0, 1] to decide which typeof low-level features is utilized for node split. If 𝜉 < 𝛾,then a quantity of optical flow features is randomly selectedas feature candidates; otherwise some randomly selectedHOG3D features comprise the set of feature candidates.Meanwhile, if 𝜃 < 𝜏, then all temporal context features areadded to the set of feature candidates. Each feature candidatedivides the training cuboids at this node into two groups,and feature candidate with the largest information gain ischosen for node split. Then the node splits into two childrennodes and each cuboid is sent to one of the children nodes.As the multitask random forest takes action category andcuboid position as two classification tasks, a random number𝜌 ∈ [0, 1] and a prior probability 𝜇 ∈ (0, 1) codeterminewhich task is used to calculated the information gain of datasplit.

A node stops splitting when it has gotten to the limitedtree depth depmax or all samples arriving at this node belongto the same action category and position, and then it isregarded as a leaf. Two vectors P = [𝑝1, 𝑝2, . . . , 𝑝𝐴] and Q =[𝑞1, 𝑞2, 𝑞3, 𝑞4] are created to store the distributions of actioncategories and cuboid positions, respectively. Here 𝑝𝑖 denotesthe posterior probability of cuboids arriving at the corre-sponding leaf node belonging to action 𝑖, and we have 𝐴actions in total. Similarly, 𝑞𝑖 represents the proportion ofcuboids at this leaf node being extracted from a particularposition. Both P and Q of a leaf node are calculated fromtraining cuboids assigned to it.The construction of a decisiontree is summarized in Algorithm 1.

Advances in Multimedia 5

5.2. Construction of Mid-Level Features. During testing, ourtask is to recognize an action instance by using videosrecorded from 𝑀 views simultaneously. With respect tomultiview subvolumes {vol𝑚,V}𝑚=1:𝑀, cuboids {𝑥𝑚,V

𝑘}𝑚=1:𝑀

sampled at position 𝑘 are handled by the correspondingmultitask random forest MTRF𝑟 = {Tree𝑚𝑟,𝑡}𝑚=1:𝑀,𝑡=1:𝑇,satisfying the condition 𝑘 ∈ cub(𝑟). Particularly, cuboid 𝑥𝑚,V

𝑘is dropped down decisions trees {Tree𝑚𝑟,𝑡}𝑡=1:𝑇; this means treeTree𝑚𝑟,𝑡 only takes the cuboid from view 𝑚 as input. Supposethat the input cuboid 𝑥𝑚,V

𝑘arrives at leaf node 𝜗(Tree𝑚𝑟,𝑡, 𝑥𝑚,V𝑘 )

of tree Tree𝑚𝑟,𝑡, with histograms P𝜗(Tree𝑚𝑟,𝑡 ,𝑥𝑚,V𝑘 ) and Q𝜗(Tree𝑚𝑟,𝑡 ,𝑥𝑚,V𝑘 )representing the distributions of action categories and cuboidpositions.Then the average distributions voted by all decisiontrees can be calculated by

P ({𝑥𝑚,V𝑘 }𝑚=1:𝑀

) = 1𝑇 ⋅ 𝑀

𝑀

∑𝑚=1

𝑇

∑𝑡=1

P𝜗(Tree𝑚𝑟,𝑡 ,𝑥𝑚,V𝑘 ),

Q ({𝑥𝑚,V𝑘 }𝑚=1:𝑀

) = 1𝑇 ⋅ 𝑀

𝑀

∑𝑚=1

𝑇

∑𝑡=1

Q𝜗(Tree𝑚𝑟,𝑡 ,𝑥𝑚,V𝑘 ).(1)

The concatenation ofP({𝑥𝑚,V𝑘

}𝑚=1:𝑀) andQ({𝑥𝑚,V𝑘 }𝑚=1:𝑀) con-stitutes an integrated local descriptor of cuboids {𝑥𝑚,V

𝑘}𝑚=1:𝑀,

denoted by h(𝑘, V) ∈ R(𝐴+4)×1. We deal with cuboidssampled at each sampling position separately and obtain aseries of histograms {h(𝑘, V)}𝑘=1:𝐾. Histograms of all the 𝐾positions are concatenated to a mid-level representation f(V)of subvolumes {vol𝑚,V}𝑚=1:𝑀 that are simultaneously recordedfrom multiple perspectives.

f (V) = [h (1, V) ; h (2, V) ; . . . ; h (𝐾, V)] . (2)

Following [25], the out-of-bag estimate [39] is employedin the construction of mid-level representations duringtraining to solve the overfitting problem. As described inAlgorithm 1, decision trees {Tree𝑚𝑟,𝑡}𝑡=1:𝑇 of view 𝑚 share anoriginal training set 𝐷𝑚𝑟 = {𝑥𝑚,V

𝑘,𝑖, 𝑦𝑖}𝑘∈cub(𝑟),V=1:𝑉𝑖 ,𝑖=1:𝑁, and

about 2/3 of cuboids in 𝐷𝑚𝑟 constitute the bootstrap trainingset 𝐷𝑚𝑟,𝑡 for tree Tree𝑚𝑟,𝑡. The construction of local descriptorh(𝑘, V, 𝑖) for training cuboids {𝑥𝑚,V

𝑘,𝑖}𝑚=1:𝑀 is the same as that

for test cuboids, except that tree Tree𝑚𝑟,𝑡 does not contribute toh(𝑘, V, 𝑖) if it was trained on 𝑥𝑚,V

𝑘,𝑖. Accordingly, we rewrite (1)

as

P ({𝑥𝑚,V𝑘,𝑖 }𝑚=1:𝑀)

=∑𝑀𝑚=1∑𝑇𝑡=1 P𝜗(Tree𝑚𝑟,𝑡 ,𝑥𝑚,V𝑘,𝑖 ) ⋅ ℓ (Tree

𝑚𝑟,𝑡, 𝑥𝑚,V𝑘,𝑖 )

∑𝑀𝑚=1∑𝑇𝑡=1 ℓ (Tree𝑚𝑟,𝑡, 𝑥𝑚,V𝑘,𝑖 ),

Q ({𝑥𝑚,V𝑘,𝑖 }𝑚=1:𝑀)

=∑𝑀𝑚=1∑𝑇𝑡=1Q𝜗(Tree𝑚𝑟,𝑡 ,𝑥𝑚,V𝑘,𝑖 ) ⋅ ℓ (Tree

𝑚𝑟,𝑡, 𝑥𝑚,V𝑘,𝑖 )

∑𝑀𝑚=1∑𝑇𝑡=1 ℓ (Tree𝑚𝑟,𝑡, 𝑥𝑚,V𝑘,𝑖 ),

(3)

where ℓ(𝑟, 𝑡, 𝑚) is an indicator function defined by

ℓ (Tree𝑚𝑟,𝑡, 𝑥𝑚,V𝑘,𝑖 )

={{{

1, if Tree𝑚𝑟,𝑡 was not trained on 𝑥𝑚,V𝑘,𝑖

0, if Tree𝑚𝑟,𝑡 was not trained on 𝑥𝑚,V𝑘,𝑖

.(4)

Similarly, the mid-level representation f(V, 𝑖) of training sub-volumes {vol𝑚,V𝑖 }𝑚=1:𝑀 is created by concatenating local des-criptors of all positions.

f (V, 𝑖) = [h (1, V, 𝑖); h (2, V, 𝑖); . . . ; h (𝐾, V, 𝑖)] . (5)

5.3. ActionRecognitionwithMid-Level Representations. Givenmid-level representations {f(V, 𝑖)}𝑖=1:𝑁,V=1:𝑉𝑖 of training sub-volumes, where f(V, 𝑖) denotes the integrated feature ofmulti-view subvolumes {vol𝑚,V𝑖 }𝑚=1:𝑀, we train a random forest clas-sifier [39] which is able to learn multiple categories discrimi-natively.

For a new action instance {f(V)}V=1:𝑉, all of the decisiontrees in random forest vote on the action category of eachsample and assign a particular action label 𝑦(V) to f(V).According tomajority voting, we predict the final action cate-gory of this instance as

𝑦∗ = arg max𝑦=1:𝐴

∑V=1:𝑉

I (𝑦 (V) = 𝑦) , (6)

where I(𝑎 = 𝑏) is an indicator function; that is, I(𝑎 = 𝑏) is 1 if𝑎 = 𝑏 and 0 otherwise.

6. Experiments

6.1. Human Action Datasets. Experiments are conducted onthe multiview IXMAS action dataset [12] and the MuHAVi-MAS dataset [31] to evaluate the effectiveness of the proposedmethod.

The IXMAS Dataset. It consists of 11 actions performed by 10actors, including “checkwatch”, “cross arms”, “scratch head”,“sit down”, “get up”, “turn around”, “walk”, “wave”, “punch”,“kick”, and “pick up”. Five cameras simultaneously recordedthese actions from different perspectives, that is, four sideviews and one top view. This dataset presents an increasedchallenge since actors can freely choose their position andorientation. Thus, there are large inter-view and intra-viewviewpoint variations of human actions in this dataset, whichmake it widely used to evaluate the performance ofmultiviewaction recognition methods.

The MuHAVi-MAS Dataset. It contains 136 manually anno-tatedsilhouette sequencesof 14primitiveactions: “CollapseLeft”,“CollapseRight”, “GuardToKick”, “GuardToPunch”, “Kick-Right”, “PunchRight”, “RunLeftToRight”, “RunRightToLeft”,“StandupLeft”, “StandupRight”, “TurnBackLeft”, “TurnBack-Right”, “WalkLeftToRight”, and “WalkRightToLeft”. Eachaction is performed several times by 2 actors and capturedby 2 cameras from different views.

6.2. Experimental Setting. Since our method takes human-centered subvolumes recorded from multiple views as input,

6 Advances in Multimedia

Input:The original training dataset𝐷𝑚𝑟 = {𝑥𝑚,V𝑘,𝑖 , 𝑦𝑖}𝑘∈cub(𝑟),V=1:𝑉𝑖 ,𝑖=1:𝑁;Predefined parameters depmax, 𝛾 ∈ (0, 1), 𝜏 ∈ (0, 1), and 𝜇 ∈ (0, 1);

Output: Decision tree Tree𝑚𝑟,𝑡;(1) Build a bootstrap dataset𝐷𝑚𝑟,𝑡 by random sampling from𝐷𝑚𝑟 with replacement;(2) Create a root node and set its depth to 1, then assign all cuboids in𝐷𝑚𝑟,𝑡 to it;(3) Initialize an unsettled node queue Υ = 0 and push the root node into Υ;(4) while Υ = 0 do(5) Pop the first node 𝜗 in Υ;(6) if depth of 𝜗 is larger than depmax or cuboids assigned to 𝜗 belong to the same action and position then(7) Label node 𝜗 as a leaf, and then calculate P andQ from cuboids at node 𝜗;(8) Add a triple (𝜗, 𝑃𝑎, 𝑃𝑐) into decision tree Tree𝑚𝑟,𝑡;(9) else(10) Initialize the feature candidate set Δ = 0;(11) if random number 𝜉 < 𝛾 then(12) Add a set of randomly selected optical flow features to Δ;(13) else(14) Add a set of randomly selected HOG3D features to Δ;(15) end if(16) if random number 𝜃 < 𝜏 then(17) Add two-dimensional temporal context features to Δ;(18) end if(19) maxgain = −∞, generate a random number 𝜌;(20) for each 𝛿𝑑 ∈ Δ do(21) if 𝜌 < 𝜇 then(22) Search for the corresponding threshold 𝜍𝑑 and compute information gain 𝑔(𝛿𝑑) in terms of action labels

of cuboids arriving at 𝜗;(23) else(24) Search for the corresponding threshold 𝜍𝑑 and compute information gain 𝑔(𝛿𝑑) in terms of positions of

cuboids arriving at 𝜗;(25) end if(26) if 𝑔(𝛿𝑑) > maxgain then(27) 𝛿∗ = 𝛿𝑑, 𝜍∗ = 𝜍𝑑;(28) end if(29) end for(30) Create left children node 𝜗𝐿 and right children node 𝜗𝑅, set their depth to dep + 1, and assign each cuboid

arriving at 𝜗 to 𝜗𝐿 or 𝜗𝑅 according to 𝛿∗ and 𝜍∗; then push node 𝛿∗ and 𝜍∗ into Υ;(31) Add a quintuple (𝜗, 𝜗𝐿, 𝜗𝑅, 𝛿∗, 𝜍∗) into decision tree Tree𝑚𝑟,𝑡;(32) end if(33) end while(34) return Decision tree Tree𝑚𝑟,𝑡;

Algorithm 1: Construction of a decision tree.

we utilize background subtraction technique to obtain hu-man silhouettes and fit a bounding box around each silhou-ette. In our implementation, a subvolume is composed of 10successive human-centered bounding boxes which are scaledto 80 × 40 pixels. We densely extract 84 cuboids from eachsubvolume with particular size (i.e., 15 × 15 × 10), and thestrides between cuboids are 5 pixels.

6.3. Experimental Results. We compare our method withstate-of-the-art methods on two datasets, and experimentalresults on the IXMAS dataset and the MuHAVi-MAS datasetare illustrated in Tables 1 and 2, respectively. In our exper-iments, the leave-one-actor-out cross-validation strategy isadopted on both datasets. We execute the random forestclassifier for 10 times and report the recognition accuracy byaveraging over the results of 10 classifiers.

6.3.1. Results on the IXMASDataset. As shown in Table 1, ourmethod significantly outperforms all the recently proposedmethods for multiview action recognition, which demon-strates the effectiveness of the proposed learning frameworkbased on multitask random forest. The confusion matrix ofmultiview action recognition results is depicted in Figure 2.We can observe from Figure 2 that the proposed methodachieves promising performance on most actions, amongwhich four actions (i.e., “sit down”, “get up”, “walk”, and“punch”) are correctly recognized. Meanwhile, some actionshave similar motion, which may result in misclassification.For example, it is difficult to distinguish actions “cross arms”,“scratch head”, and “wave”, since they all involve motion ofthe upper limb. Similarly, actors crouch in both actions of “sitdown” and action “pick up”, which may be a possible reasonfor the misclassification of “pick up”.

Advances in Multimedia 7

0.90

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.88

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.68

0.00

0.00

0.00

0.00

0.10

0.00

0.00

0.00

0.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.15

0.00

0.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.90

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.10

1.00

0.00

0.00

0.07

0.00

0.07

0.13

0.30

0.00

0.00

0.00

0.00

0.80

0.00

0.00

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.10

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.93

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.85Pick up

Kick

Punch

Wave

Walk

Turn around

Get up

Sit down

Scratch head

Cross arms

Check watch

Kick

Wal

k

Wav

e

Get

up

Punc

h

Pick

up

Sit d

own

Cros

s arm

s

Turn

arou

nd

Scra

tch

head

Chec

k w

atch

Figure 2: Confusion matrix of our multiview action recognition method on the IXMAS dataset.

Table 1: Action recognition accuracy comparison with state-of-the-art methods for bothmultiview action recognition and single-view actionrecognition on the IXMAS dataset.

Method View 1 View 2 View 3 View 4 View 5 Average MultiviewJunejo et al. [26] 76.4% 77.6% 73.6% 68.8% 66.1% 72.5% 72.7%Weinland et al. [27] 85.8% 86.4% 88.0% 88.2% 74.7% 84.6% 83.5%Wu et al. [28] 81.9% 80.1% 77.1% 77.6% 73.4% 78.0% –Chaaraoui et al. [18] – – – – – – 85.9%Liu et al. [25] 84.5% 84.7% 88.0% 82.9% 83.4% 84.7% 88.0%Pehlivan and Forsyth [11] 81.3% 86.3% 86.3% 85.9% 77.6% 83.5% 72.5%Aryanfar et al. [29] – – – – – – 88.1%Hsu et al. [17] – – – – – – 81.8%Chun and Lee [15] – – – – – – 83.0%Wang et al. [30] – – – – – – 84.9%Sargano et al. [20] – – – – – – 89.7%Our method 89.6% 88.4% 85.8% 78.2% 86.9% 85.8% 90.3%

The proposed method is also compared with othermethods for single-view action recognition, and the resultsare summarized in Table 1. Videos of different views arehandled separately, and a mid-level representation is createdfor a single video. Concretely, we build a set of multitaskrandom forests for each view by using cuboids extracted fromsubvolumes of this view. Accordingly, all the decision trees ina multitask random forest share an original training datasetcomposed of cuboids from a certain view. It is observed thatthe proposed method performs better than [26, 28] on all ofthe five views. We can see that our method is competitive

with [11, 25, 27] on two views and achieves much betterperformance on three views. However, our method is ableto outperform the above three methods by fusing videos ofmultiple views into an integrated representation.

6.3.2. Results on the MuHAVi-MAS Dataset. As is shownin Table 2, our method achieves much better performancethan the listed methods on the MuHAVi-MAS dataset.The promising results demonstrate the effectiveness of theproposed method. Figure 3 reveals the confusion matrix ofour results.We can see that ourmethod can correctly identify

8 Advances in Multimedia

1.00

0.10

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.90

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.50

0.13

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.25

0.85

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.23

0.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

1.00

Kick

Righ

t

Punc

hRig

ht

Stan

dupL

e�

Col

laps

eLe�

Gua

rdTo

Kick

Turn

Back

Le�

Stan

dupR

ight

Col

laps

eRig

ht

Turn

Back

Righ

t

Gua

rdTo

Punc

h

RunL

e�To

Righ

t

RunR

ight

ToLe

�

Wal

kRig

htTo

Le�

Wal

kLe�

ToRi

ght

WalkRightToLe�

WalkLe�ToRight

TurnBackRight

TurnBackLe�

StandupRight

StandupLe�

RunRightToLe�

RunLe�ToRight

PunchRight

KickRight

GuardToPunch

GuardToKick

CollapseRight

CollapseLe�

Figure 3: Confusion matrix of our action recognition method on the MuHAVi-MAS dataset.

eleven of the fourteen actions, that is, “CollapseLeft”, “Kick-Right”, “PunchRight”, “RunLeftToRight”, “RunRightToLeft”,“StandupLeft”, “StandupRight”, “TurnBackLeft”, “TurnBack-Right”, “WalkLeftToRight”, and “WalkRightToLeft”. It is alsoobservable that ourmethoddoes not dowell in distinguishing“GuardToKick” and “GuardToPunch”, since they have verysimilar motion.

6.4. Effects of Parameters. In this section, we evaluate theeffect of two parameters in the construction of multitask ran-dom forest on the IXMAS dataset.

In consideration of the computation cost, we limit thedepth of each decision tree to depmax, and Figure 4 depictsthe accuracy of both single-view action recognition on fiveperspectives and multiview action recognition with differenttree depths. Generally, the curves first rise and then declineslightly with the increasement of tree depth. One possiblereason is that large decision trees may overfit training data.Meanwhile, it is interesting to observe that the depths fromwhich the forests are overfitting vary on different views. Fur-thermore, we can observe fromFigure 4 thatmultiview actionrecognition method is able to achieve better performancethan single-view action recognition with all tree depths,which demonstrates the effectiveness of the multiview fusionscheme.

Another key parameter of our method is the prior prob-ability 𝜇 ∈ (0, 1). Our multitask random forest is designed

0.700.720.740.760.780.800.820.840.860.880.900.92

Accu

racy

View 1View 2View 3

View 4View 5Multiview

6 7 8 10 11 13 14 16 175Depth

Figure 4: Action recognition accuracy with different tree depths onthe IXMAS dataset.

to solve two tasks including action recognition and positionclassification. For the purpose of node split, we introducethe parameter 𝜇 to select a certain task for calculating the

Advances in Multimedia 9

Table 2: Action recognition accuracy of different methods on theMuHAVi-MAS dataset.

Method AccuracySingh et al. [31] 61.8%Orrite et al. [32] 75.0%Cheema et al. [33] 75.5%Murtaza et al. [34] 81.6%Our method 91.2%

= 1 = 0.95 = 0.9 = 0.6

= 0.8 = 0.7

0.70

0.72

0.74

0.76

0.78

0.80

0.82

0.84

0.86

0.88

0.90

View 2 View 3 View 4 View 5 AverageView 1

Figure 5: Action recognition accuracy of different values of 𝜇 on theIXMAS dataset.

information gain of data split. More concretely, 𝜇 denotes theprobability that the action recognition task is selected at eachnode. We tune the value of 𝜇 to investigate how it affects theperformance and summarize the action recognition results inFigure 5. It should be noted that multitask random forest isreduced to random forest which takes action recognition asits only task if 𝜇 is set to 1. From Figure 5 we can observe thataction recognition results of multitask random forest (e.g.,𝜇 = 0.8, 0.9, 0.95) are better than that of single-task randomforest (i.e., 𝜇 = 1), which demonstrates the effectiveness ofour multitask random forest learning framework.

7. Conclusion

We presented a learning framework based on multitaskrandom forest in order to exploit a discriminative mid-levelrepresentation for videos from multiple views. Our methodstarts from multiview subvolumes with fixed size, each ofwhich is composed of continuous human-centered figures.Densely sampled spatiotemporal cuboids are extracted fromsubvolumes and three types of low-level descriptors areutilized to capture the motion, appearance, and temporalcontext of each cuboid. Then a multitask random forest isbuilt upon cuboids from multiple views that are sampled atfour adjacent positions, taking action category and position

as two tasks. Each cuboid is classified by its correspondingrandom forest, and a fusion strategy is employed to createan integrated histogram for describing cuboids sampled ata certain position of multiview subvolumes. Concatenationof histograms for different positions is utilized as a mid-levelrepresentation for subvolumes simultaneously recorded frommultiple views. Experiments on the IXMAS action datasetshow that the proposed method is able to achieve promisingperformance.

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper.

Acknowledgments

This work was supported in part by the Natural ScienceFoundation of China (NSFC) under Grants no. 61602320and no. 61170185, Liaoning Doctoral Startup Project underGrants no. 201601172 and no. 201601180, Foundation of Lia-oning Educational Committee under Grants no. L201607,no. L2015403, and no. L2014070, and the Young ScholarsResearch Fund of SAU under Grant no. 15YB37.

References

[1] I. Laptev, “On space-time interest points,” International Journalof Computer Vision, vol. 64, no. 2-3, pp. 107–123, 2005.

[2] I. Laptev,M.Marszałek, C. Schmid, and B. Rozenfeld, “Learningrealistic human actions frommovies,” in Proceedings of the 26thIEEE Conference on Computer Vision and Pattern Recognition(CVPR ’08), June 2008.

[3] H. Wang, A. Klaser, C. Schmid, and C.-L. Liu, “Action recogni-tion by dense trajectories,” in Proceedings of the IEEEConferenceon Computer Vision and Pattern Recognition (CVPR ’11), pp.3169–3176, June 2011.

[4] F. C. Heilbron, V. Escorcia, B. Ghanem, and J. C. Niebles, “Acti-vityNet: A large-scale video benchmark for human activityunderstanding,” in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition, CVPR 2015, pp. 961–970, USA, June 2015.

[5] C. Liu, X. Wu, and Y. Jia, “A hierarchical video description forcomplex activity understanding,” International Journal of Com-puter Vision, vol. 118, no. 2, pp. 240–255, 2016.

[6] R. Poppe, “Vision-based human motion analysis: an overview,”Computer Vision and Image Understanding, vol. 108, no. 1-2, pp.4–18, 2007.

[7] R. Poppe, “A survey on vision-based human action recognition,”Image and Vision Computing, vol. 28, no. 6, pp. 976–990, 2010.

[8] D. Weinland, R. Ronfard, and E. Boyer, “A survey of vision-based methods for action representation, segmentation andrecognition,” Computer Vision and Image Understanding, vol.115, no. 2, pp. 224–241, 2011.

[9] M. Vrigkas, C. Nikou, and I. A. Kakadiaris, “A review of humanactivity recognition methods,” Frontiers in Robotics and AI, vol.2, article 28, 2015.

[10] H. Xu, Q. Tian, Z. Wang, and J. Wu, “A survey on aggregatingmethods for action recognition with dense trajectories,”Multi-media Tools and Applications, vol. 75, no. 10, pp. 5701–5717, 2016.

10 Advances in Multimedia

[11] S. Pehlivan and D. A. Forsyth, “Recognizing activities in multi-ple views with fusion of frame judgments,” Image and VisionComputing, vol. 32, no. 4, pp. 237–249, 2014.

[12] D. Weinland, R. Ronfard, and E. Boyer, “Free viewpoint actionrecognition using motion history volumes,” Computer Visionand Image Understanding, vol. 104, no. 2-3, pp. 249–257, 2006.

[13] R. Souvenir and J. Babbs, “Learning the viewpoint manifold foraction recognition,” in Proceedings of the 26th IEEE Conferenceon Computer Vision and Pattern Recognition, CVPR, USA, June2008.

[14] M. B. Holte, T. B. Moeslund, N. Nikolaidis, and I. Pitas, “3D hu-man action recognition for multi-view camera systems,” inProceedings of the 2011 International Conference on 3D Imaging,Modeling, Processing, Visualization and Transmission, 3DIM-PVT 2011, pp. 342–349, China, May 2011.

[15] S. Chun and C.-S. Lee, “Human action recognition using histo-gram of motion intensity and direction from multiple views,”IET Computer Vision, vol. 10, no. 4, pp. 250–256, 2016.

[16] D. Weinland, E. Boyer, and R. Ronfard, “Action recognitionfrom arbitrary views using 3D exemplars,” in Proceedings of theIEEE 11th International Conference on Computer Vision (ICCV’07), pp. 1–7, October 2007.

[17] Y.-P. Hsu, C. Liu, T.-Y. Chen, and L.-C. Fu, “Online view-invariant human action recognition using rgb-d spatio-temporal matrix,” Pattern Recognition, vol. 60, pp. 215–226,2016.

[18] A. A. Chaaraoui, P. Climent-Perez, and F. Florez-Revuelta, “Sil-houette-based human action recognition using sequences ofkey poses,” Pattern Recognition Letters, vol. 34, no. 15, pp. 1799–1807, 2013.

[19] A. K. S. Kushwaha, S. Srivastava, and R. Srivastava, “Multi-viewhuman activity recognition based on silhouette and uniformrotation invariant local binary patterns,” Multimedia Systems,vol. 23, no. 4, pp. 451–467, 2017.

[20] A. B. Sargano, P. Angelov, and Z. Habib, “Human action recog-nition from multiple views based on view-invariant featuredescriptor using support vector machines,” Applied Sciences(Switzerland), vol. 6, no. 10, article no. 309, 2016.

[21] F.Murtaza,M.H. Yousaf, and S. A.Velastin, “Multi-viewhumanaction recognition using 2D motion templates based on MHIsand their HOG description,” IET Computer Vision, vol. 10, no.7, pp. 758–767, 2016.

[22] Z. Gao, S. H. Li, G. T. Zhang, Y. J. Zhu, C. Wang, and H. Zhang,“Evaluation of regularized multi-task leaning algorithms forsingle/multi-view human action recognition,”Multimedia Toolsand Applications, vol. 76, no. 19, pp. 20125–20148, 2017.

[23] T. Hao, D. Wu, Q. Wang, and J.-S. Sun, “Multi-view represen-tation learning for multi-view action recognition,” Journal ofVisual Communication and Image Representation, vol. 48, pp.453–460, 2017.

[24] J. Lei, G. Li, J. Zhang, Q. Guo, and D. Tu, “Continuous actionsegmentation and recognition using hybrid convolutional neu-ral network-hidden Markov model model,” IET ComputerVision, vol. 10, no. 6, pp. 537–544, 2016.

[25] C. Liu, M. Pei, X. Wu, Y. Kong, and Y. Jia, “Learning a discrimi-native mid-level feature for action recognition,” Science ChinaInformation Sciences, vol. 57, no. 5, pp. 1–13, 2014.

[26] I. N. Junejo, E. Dexter, I. Laptev, and P. Perez, “Cross-View Ac-tion Recognition from Temporal Self-similarities,” in ComputerVision – ECCV 2008, vol. 5303 of Lecture Notes in ComputerScience, pp. 293–306, Springer Berlin Heidelberg, Berlin, Hei-delberg, 2008.

[27] D. Weinland, M. Ozuysal, and P. Fua, “Making action recogni-tion robust to occlusions and viewpoint changes,” in EuropeanConference on Computer Vision, pp. 635–648, 2010.

[28] X. Wu, D. Xu, L. Duan, and J. Luo, “Action recognition usingcontext and appearance distribution features,” in Proceedingsof the 2011 IEEE Conference on Computer Vision and PatternRecognition, CVPR 2011, pp. 489–496, USA, June 2011.

[29] A. Aryanfar, R. Yaakob, A. A. Halin, M. N. Sulaiman, K. A. Kas-miran, and L. Mohammadpour, “Multi-view human action re-cognition using wavelet data reduction and multi-class classifi-cation,” Procedia Computer Science, vol. 62, no. 27, pp. 585–592,2015.

[30] W. Wang, Y. Yan, L. Zhang, R. Hong, and N. Sebe, “Collabo-rative Sparse Coding for Multiview Action Recognition,” IEEEMultiMedia, vol. 23, no. 4, pp. 80–87, 2016.

[31] S. Singh, S. A. Velastin, and H. Ragheb, “MuHAVi: a multicam-era human action video dataset for the evaluation of actionrecognition methods,” in Proceedings of the 7th IEEE Interna-tional Conference on Advanced Video and Signal Based (AVSS’10), pp. 48–55, September 2010.

[32] C. Orrite, M. Rodriguez, E. Herrero, G. Rogez, and S. A. Velast-in, “Automatic segmentation and recognition of human actionsin monocular sequences,” in Proceedings of the 22nd Interna-tional Conference on Pattern Recognition, ICPR 2014, pp. 4218–4223, Sweden, August 2014.

[33] S. Cheema, A. Eweiwi, C.Thurau, andC. Bauckhage, “Action re-cognition by learning discriminative key poses,” in Proceedingsof the Proceeding of the IEEE International Conference on Com-puter Vision Workshops (ICCV ’11), pp. 1302–1309, Barcelona,Spain, November 2011.

[34] F. Murtaza, M. H. Yousaf, and S. A. Velastin, “Multi-view Hu-man Action Recognition Using Histograms of Oriented Gra-dients (HOG) Description of Motion History Images (MHIs),”in Proceedings of the 13th International Conference on Frontiersof Information Technology, FIT 2015, pp. 297–302, Pakistan,December 2015.

[35] A. A. Efros, A. C. Berg, G. Mori, and J. Malik, “Recognizingaction at a distance,” in Proceedings of the IEEE InternationalConference on Computer Vision, pp. 726–733, Nice, France,October 2003.

[36] B. D. Lucas and T. Kanade, “An iterative image registration tech-nique with an application to stereo vision,” in Proceedings of theInternational Joint Conference on Artificial Intelligence, vol. 81,pp. 674–679, 1981.

[37] A. Klaser, M. Marszałek, and C. Schmid, “A spatio-temporaldescriptor based on 3D-gradients,” in Proceedings of the 19thBritish Machine Vision Conference (BMVC ’08), pp. 995–1004,September 2008.

[38] N. Dalal and B. Triggs, “Histograms of oriented gradients forhuman detection,” in Proceedings of the IEEE Computer SocietyConference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893, June 2005.

[39] L. Breiman, “Random forests,”Machine Learning, vol. 45, no. 1,pp. 5–32, 2001.

International Journal of

AerospaceEngineeringHindawiwww.hindawi.com Volume 2018

RoboticsJournal of

Hindawiwww.hindawi.com Volume 2018

Hindawiwww.hindawi.com Volume 2018

Active and Passive Electronic Components

VLSI Design

Hindawiwww.hindawi.com Volume 2018

Hindawiwww.hindawi.com Volume 2018

Shock and Vibration

Hindawiwww.hindawi.com Volume 2018

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawiwww.hindawi.com Volume 2018

Hindawiwww.hindawi.com Volume 2018

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwww.hindawi.com

Volume 2018

Hindawi Publishing Corporation http://www.hindawi.com Volume 2013Hindawiwww.hindawi.com

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of

Hindawiwww.hindawi.com Volume 2018

Hindawiwww.hindawi.com

Journal ofEngineeringVolume 2018

SensorsJournal of

Hindawiwww.hindawi.com Volume 2018

International Journal of

RotatingMachinery

Hindawiwww.hindawi.com Volume 2018

Modelling &Simulationin EngineeringHindawiwww.hindawi.com Volume 2018

Hindawiwww.hindawi.com Volume 2018

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawiwww.hindawi.com Volume 2018

Hindawiwww.hindawi.com Volume 2018

Navigation and Observation

International Journal of

Hindawi

www.hindawi.com Volume 2018

Advances in

Multimedia

Submit your manuscripts atwww.hindawi.com