Wheelchair-user Detection Combined with Parts-based Tracking

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1 Wheelchair-user Deecion Combined wih Pars-based Tracking Ukyo Tanikawa 1, Yasuomo Kawanishi 1, Daisuke Deguchi 2,IchiroIde 1, Hiroshi Murase 1 and Ryo Kawai 3 1 Graduae School of Informaion Science, Nagoya Universiy, Furo-cho, Chikusa-ku, Nagoya-shi, Aichi, Japan 2 Informaion Sraegy Office, Nagoya Universiy, Furo-cho, Chikusa-ku, Nagoya-shi, Aichi, Japan 3 NEC Corporaion, 1753 Shimo Numabe, Nakahara-ku, Kawasaki-shi, Kanagawa, Japan anikawau@murase.m.is.nagoya-u.ac.jp, {kawanishi, ide, murase}@is.nagoya-u.ac.jp, ddeguchi@nagoya-u.jp, r-kawai@az.jp.nec.com Keywords: Absrac: Objec Deecion, Wheelchair User, Crowded Scene, Pars-based Tracking. In recen years, here has been an increasing demand for auomaic wheelchair-user deecion from a surveillance video o suppor wheelchair users. However, i is difficul o deec hem due o occlusions by surrounding pedesrians in a crowded scene. In his paper, we propose a deecion mehod of wheelchair users robus o such occlusions. Concreely, in case he deecor canno a deec wheelchair user, he proposed mehod esimaes his/her locaion by pars-based racking based on pars relaionship hrough ime. This makes i possible o deec occluded wheelchair users even hough he/she is heavily occluded. As a resul of an experimen, he deecion of wheelchair users wih he proposed mehod achieved he highes accuracy in crowded scenes, compared wih comparaive mehods. 1 INTRODUCTION In recen years, various effors are being made o realize a symbioic sociey where people wih disabiliies can enjoy heir lives acively. For example, many public faciliies have become handicapped-accessible o suppor wheelchair users. However, here are sill many scenes where hey need help from ohers. In such cases, o provide appropriae suppor as needed, here has been an increasing demand for a sysem o deec wheelchair users auomaically from surveillance video. However, in an acual environmen such as railway saions, many pedesrians ofen surround wheelchair users. Figure 1 shows an example of a wheelchair user moving in a crowded scene. In a crowded scene like his, here is a problem ha deecion ofen fails since he whole body of a wheelchair user is no visible because of occlusions caused by surrounding pedesrians. In his paper, we aim o deec occluded wheelchair users in a crowded scene, and propose a deecion mehod robus o occlusions. A deecor ofen suffers when he arge is heavily occluded. Tracking based on heir pas posiions enables us o locae occluded arges even when he deecor canno deec hem. However, when he racking arge is occluded, he racking accuracy declines. Figure 1: Example of a wheelchair user moving in a crowded scene. Occlusions by surrounding pedesrians are ofen observed. We have observed ha some pars of a wheelchair user are visible even if his/her body is almos occluded, because he widh and he deph of wheelchair users are larger han hose of pedesrians in general. If he pars are visible, we can roughly esimae his/her bounding box. In his paper, based on he observaion, we propose a mehod which combines deecion by a deecor wih pars-based racking. When a radiional deecor could no deec a arge due o occlusions, he proposed mehod can esimae is locaion based on pars-racking resuls. Since he size of he pars is small in general, i is difficul o disinguish hem from oher objecs. Hence, a pars racker which only considers heir appearance can drif easily. To reduce he drif, we in- Tanikawa, U., Kawanishi, Y., Deguchi, D., Ide, I., Murase, H. and Kawai, R. Wheelchair-user Deecion Combined wih Pars-based Tracking. In Proceedings of he 12h Inernaional Join Conference on Compuer Vision, Imaging and Compuer Graphics Theory and Applicaions (VISIGRAPP 2017) - Volume 5: VISAPP, pages ISBN: Copyrigh c 2017 by SCITEPRESS Science and Technology Publicaions, Lda. All righs reserved 165

2 VISAPP Inernaional Conference on Compuer Vision Theory and Applicaions roduce par racking confidence and pars relaionship hrough ime; The proposed mehod calculaes he racking confidence of each par of a arge. The pars wih high confidence are racked based on heir appearances. The posiions of he pars wih low confidence are prediced based on heir pas rajecories and iner-pars posiional relaionships. In summary, our conribuions include he proposal of: A framework of wheelchair-user deecion robus o occlusions by combining a deecor wih parsbased racking. A pars-based racking mehod which considers rajecories and iner-pars posiional relaionships o predic posiions of pars wih low confidence. The res of he paper refers o relaed works in Secion 2, describes he proposed framework in Secion 3 and he proposed pars-based racking mehod in Secion 4, repors evaluaion resuls in Secion 5, and concludes he paper in Secion 6. 2 RELATED WORKS Dalal and Triggs proposed an objec deecion mehod using Hisogram of Oriened Gradiens (HOG) feaures (Dalal and Triggs, 2005). HOG is a feaure descripor robus o local shape deformaions, illuminaion variaions, and effecs of shades. However, HOG canno handle large pose deformaions. In conras, Felzenszwalb e al. proposed an objec deecion mehod using Deformable Par Model (DPM), which represens an objec model wih a se of pars (Felzenszwalb e al., 2010). DPM is robus o pose deformaions by considering fine shape and posiion of each par. The posiion is reaed as laen variables and auomaically learned by using Laen SVM (Felzenszwalb e al., 2010). However, DPM has a problem ha is deecion accuracy degrades when he pars are occluded. Myles e al. proposed a deecion mehod specialized for wheelchair users based on he deecion of wheels and faces of heir users (Myles e al., 2002). In his mehod, wheels of wheelchairs are deeced by using he Hough ransform, and faces of heir users are deeced based on heir color feaures. Then, heir 3-D poses are consruced by 2-D ellipse projecion. However, his mehod needs accurae calibraion in advance. Huang e al. proposed a mehod of wheelchair-user deecion from a single camera wih no calibraion (Huang e al., 2010). This mehod uses HOG and Conras Conex Hisogram feaures (Huang e al., 2006), and a hierarchical cascade classifier using AdaBoos is buil. However, his mehod does no consider occlusions of wheelchair users, so in a crowded scene, i canno deec hem accuraely. Henriques e al. proposed a mehod for single objec racking using Kernelized Correlaion Filer (KCF) racker (Henriques e al., 2015). KCF racker achieves good performance wih high speed. I is a mehod based on kernel ridge regression, and is a kind of correlaion-filer-based racking mehods (Bolme e al., 2009; Bolme e al., 2010). Correlaion-filerbased rackers can calculae racking confidence using he Peak-o-Sidelobe Raio (PSR), which quanifies he srengh of correlaion peak relaive o an area around he peak in a response map (Bolme e al., 2010). Zhang e al. proposed a mehod for muli-person racking combining person deecion by a deecor wih visual objec racking (Zhang e al., 2012). This mehod represens arge appearance wih a se of emplaes gahered from deecions, and racking is performed by alernaing mean-shif racking and Kalman filering. This enables an esimaion of heir locaion even if he deecor canno deec hem. However, his mehod does no ake ino accoun occlusions of racking arges, so in a crowded scene, i canno rack hem accuraely. There are racking mehods which explicily consider he arge s parial occlusions. Pan and Hu proposed a racking mehod which handles occlusions by exploiing spaio-emporal conex informaion (Pan and Hu, 2007). However, his mehod does no consider heavy occlusions. In summary, hese convenional mehods canno handle heavy occlusions of wheelchair users well. 3 FRAMEWORK OF WHEELCHAIR USERS DETECTION COMBINED WITH PARTS-BASED TRACKING As menioned above, deecion of wheelchair users in a crowded scene is challenging due o heavy occlusions. These occlusions are ofen caused by surrounding pedesrians. Since i is difficul o deec occluded arges from only a single frame, we inroduce a framework wih pars-based racking across muliple frames, which is inroduced in Secion 4. Figure 2 shows he process flow of he proposed framework. In he raining phase, a wheelchair-user deecor is rained. In he deecion phase, wheelchair users are deeced from each frame of an inpu sequence by using he rained deecor. The deecions 166

3 Wheelchair-user Deecion Combined wih Pars-based Tracking Figure 2: Process flow of he proposed framework. Figure 4: Example of he deecion associaion process. Figure 3: Example of deecion using DPM. from consecuive frames are associaed o consruc heir rajecories. When some deecions were no associaed, he proposed pars-based racking is performed o esimae heir locaions. 3.1 Deecion by a Pars-based Deecor Full bodies of wheelchair users and heir pars in each frame of an inpu sequence are deeced by a parsbased deecor. In his paper, we use DPM (Felzenszwalb e al., 2010) which can simulaneously deec boh of hem. Figure 3 shows an example of deecions using DPM for wheelchair users. In he raining phase, a DPM deecor for wheelchair users is rained wih many posiive and negaive images. In he deecion phase, wheelchair users are deeced from each frame of inpu sequences by he rained DPM deecor. 3.2 Associaion of he Deecions For each frame of an inpu sequence, deecion resuls of wheelchair users are associaed o consruc heir rajecories. Le D = {d (1),d(2),...,d(n ) } be he final deecion resuls obained wih he proposed mehod in he ( 1)-h frame, and D = {d (1),d (2),...,d (n ) } be he deecion resuls obained wih he pars-based deecor in he -h frame. Firs, he similariy beween each pair in D and D is calculaed o find similar deecion resuls. In his paper, we use an overlap raio Ω(d (i) of deecions (d (i) ) as he similariy, which is defined as follows:,d( Ω(d (i),d(,d( ) beween he pair )= d(i) d (i) d( d(. (1) The similariy S(d (i),d( ) beween d (i) and d( is defined as follows: { S(d (i) (i),d( )= Ω(d,d( ) if Ω(d (i),d( )>θ Ω. 0 oherwise (2) Deecion resuls are associaed by selecing he pair of deecions which has a larger similariy han a hreshold. Figure 4 shows an example of associaion of he deecion resuls. 3.3 Esimaion using Pars-based Tracking When he deecor los he arge deeced more han θ d imes coninuously due o occlusions, his/her posiion is esimaed by racking. While his/her fullbodyracking is difficul due o occlusions, some pars of he body are ofen visible even if i is almos occluded. We perform he pars-based racking inroduced in Secion 4 o esimae his/her posion. Deecion resuls of pars by he pars-based deecor are uilized as an iniial bounding box of pars-racking. Pars-based racking is conduced based on he posiion of he arge in he ( 1)-h frame, and is posiion afer he -h frame is esimaed from is pas rajecory and he posiion of is confidenly-racked pars. Pars-based racking coninues up o f 1 frames. I erminaes in he following cases: 167

4 VISAPP Inernaional Conference on Compuer Vision Theory and Applicaions Figure 5: Process flow of he proposed pars-based racking mehod. The racked resul and he deecion resul were associaed successfully, i.e., he arge was deeced by he pars-based deecor again before f 1 frames passed. All pars of he arge were occluded for f 2 consecuive frames. This can suppress false deecions caused by failed pars-based racking. 4 PARTS-BASED TRACKING Pars-based approach is robus o he arge s occlusions. Since he size of pars is small, i is difficul o disinguish hem from oher objecs. Hence, parsbased racking which only considers heir appearance can drif easily. To rack hem accuraely, he proposed pars-based racking mehod compensaes he posiion of pars considering heir pas rajecories and iner-pars posiional relaionships. Figure 5 shows he process flow of he proposed pars-based racking mehod. Firs, he proposed mehod racks each par based on is appearance and calculaes each racking confidence. If he racking confidence of he par is high, is appearance model is updaed. If he confidence is low, is posiion is exrapolaed based on heir pas rajecories and inerpars posiional relaionships. We inegrae hese informaion ino a score map on he cener posiion of he arge, and adop he posiion ha maximizes his score. In he end, he full-body bounding box is esimaed based on he pars locaions. 4.1 Appearance-based Tracking and Confidence Calculaion The proposed mehod racks each par of a arge using KCF racker (Henriques e al., 2015). KCF racks a arge convolving an inpu image wih a filer designed o produce correlaion peaks for he arge in a response map, while producing low responses o background. The filer is updaed over ime o adap o appearance change. The proposed mehod racks each par based on is appearance and calculaes each racking confidence. When i is difficul o rack he par (e.g., is size is small, or i is occluded), is confidence ends o ge lower. Therefore, we change he racking mehod according o he confidence. In he following explanaion, we describe he process for each par. Firs, he response map of KCF racker for he par is caluculaed. Nex, racking confidence is calculaed from he response map. We uilize he Peak-o-Sidelobe Raio (PSR) of he response map as he racking confidence. PSR quanifies he srengh of correlaion peak relaive o he sidelobe area in a response map. In his paper, we define he sidelobe as a square area around he peak which has 15% area of he response map. The pars which have higher PSR values han a hreshold θ PSR are recognized as highly confiden. The posiions of he pars wih high confidence are se o be he posiions of correlaion peaks in heir response map. In conras, he racked resuls which has lower confidence han he hreshold are unreliable. We esimae heir posiions by a mehod inroduced in Secion 4.2. Noe ha he appearance model of each KCF racker is updaed over ime, bu updaing a lowly confiden arge s model leads o he decline of racking accuracy. Therefore, we updae models of pars only when hey have high confidence. 4.2 Predicion of Pars Posiions The posiions of he pars wih low confidence are esimaed based on heir rajecories and iner-pars posiional relaionship. We inegrae hese informaion ino score map S on he cener posiion of he par, and adop he posiion ha maximizes his score. Le p (i) (i = 1,...,n) be he i-h par of he arge, P l be he se of pars wih low confidence, and P h be he se of pars wih high confidence in he curren frame. Noe ha P l P h = Ø, P l P h = n holds. The cener posiion ˆx (i) of he low-confiden par p (i) P l in he -h frame is esimaed as follows: where x (i) =(x (i) ˆx (i),y (i) p (i) in he image coordinae. ˆx (i) = arg maxs(x (i) ), (3) x (i) ) is he cener posiion of he par will be he posiion 168

5 Wheelchair-user Deecion Combined wih Pars-based Tracking Figure 6: Calculaion of he score on he posiion of he par recognized as occluded. ha maximizes he score S(x (i) ). The widh and he heigh of he esimaed bounding box of he par are se o be he same as hose in he ( 1)-h frame. The score map S on posiion x (i) of par p (i) P l in he -h frame is defined as follows: S(x (i) )= P b (x (i) x (, x (i), x( ) p ( P h +λp u (x (i) x (i), x(i) 2 ). (4) The firs erm in he righ-hand side of Equaion (4) is he sum of scores on he posiion of par p (i) based on iner-pars posiional relaionships in he ( 1)-h frame. The more pars here are wih high confidence, he more reliable and larger his score is. The second erm in he righ-hand side of he Equaion (4) is he scores based on is rajecory. λ is he rade-off beween he firs erm and he second erm. The score map P b based on iner-pars posiional relaionship beween p ( P h and p (i) P l in he - h frame is modeled by he sum of bivariae normal disribuion N (μ b,(i) mean vecor μ b,(i) Σ b,(i) are defined as follows:, Σ b,(i) ) as shown in Figure 6. The and he variance-covariance marix μ b,(i) = x ( ), (5) ( ) σ Σ b,(i) 2 = x,,(i) 0 0 σ 2. (6) y,,(i) +(x (i) x( The mean vecor μ b,(i) is he sum of he posiion of p ( and an offse vecor from p ( o p (i) in he ( 1)-h frame. Diagonal componens σ x,,(i),σ y,,(i) of he variance-covariance marix Σ b,(i) is calculaed as Figure 7: Example of he esimaion of he full-body bounding box from par bounding boxes. follows: σ x,,(i) = w s σ y,,(i) = h s ( ) PSR p (, 1 p (k) P h PSR ( p (k), ), (7) ( ) PSR p (, 1 p (k) P h PSR ( p (k), ), (8) where w and h are he widh and he heigh of p (i) in he ( 1)-h frame respecively. The larger hey are, he shorer and wider he normal disribuion becomes. PSR(p (,) denoes he PSR of p ( in he -h frame. The larger PSR(p (,) relaive o ha of oher pars wih high confidence encourages smaller σ x,,(i) and σ y,,(i), i.e., he lower he confidence is, he shorer and wider he disribuion becomes. s is a scale parameer. The score map P u based on he rajecory of p (i) is also modeled by he bivariae normal disribuion N (μ u,(i), Σ u,(i) ). The mean μ u,(i) of he disribuion is defined as follows: μ u,(i) μ u,(i) = x (i) +(x(i) x(i) 2 ). (9) is he sum of he posiion in he ( 1)-h frame and he displacemen vecor from he ( 2)-h frame o he ( 1)-h frame. The variance-covariance marix Σ u,(i) is a diagonal marix same as Equaion 6, where σ x,,(i) and σ y,,(i) are se o be in proporion o he widh and he heigh of p (i) in he ( 1)-h frame, respecively. 4.3 Full-body Bounding Box Esimaion In each frame, he racked resuls of pars are pu ogeher o esimae a full-body bounding box of he arge. The bounding box of he whole arge is defined o be a minimum bounding box including all bounding boxes of pars. Figure 7 shows an example of his inegraion. The inner small recangles are bounding boxes of pars, and he ouer large recangle is he esimaed bounding box of he whole arge. 169

6 VISAPP Inernaional Conference on Compuer Vision Theory and Applicaions (a) Roo filers Figure 8: Example of posiive and negaive raining samples. 5 EXPERIMENT (b) Par filers 5.1 Experimenal Condiion To evaluae he effeciveness of he proposed mehod in he deecion of wheelchair users under a crowded scene, we conduced an experimen. In he experimen, hey were deeced from video sequences capured in an environmen where many pedesrians surrounded hem. We compared he following mehods: DPM: Using only DPM deecor. DPM + Full-body racking: Using DPM deecor combined wih full-body racking of arges. DPM + Muli-racker: Using DPM deecor combined wih muli-person racker (Zhang e al., 2012). DPM + Pars-racker (Proposed mehod): Using DPM deecor combined wih pars-based racking. For he evaluaion of DPM + Muli-racker, we used he publicly available implemenaion (Zhang e al., 2013) provided by he auhors. We se he parameers for racking as f 1 = 20 frames and f 2 = 15 frames. The overlap raio beween deecions by each mehod and he ground ruh was calculaed. Deecions are considered o be correc when hey overlapped more han 50% wih he ground-ruh bounding box. As an evaluaion crierion of deecion accuracy, we employed precision, recall, and F-measure. 5.2 Daases Training Daa In he experimen, 2,400 images of wheelchair users capured boh indoors and oudoors were used as posiive samples o rain he DPM deecor. For each raining image, we annoaed bounding boxes of wheelchair users manually. As negaive samples, we (c) Deformaion coss Figure 9: Three-componens DPM rained for wheelchair users. prepared 4,800 images randomly cropped from he background of he raining images. Figure 8 shows an example of posiive and negaive raining samples. The larger recangle in blue shows a posiive sample, and he smaller recangle in red shows a negaive sample Tes Daa As es daa, we prepared seven video sequences capured oudoors. The size of each frame in he es sequences was 1,280 1,024 pixels. The lengh of each sequence was from approximaely 30 seconds o 1 minues, wih a frame rae of 6 fps. The number of images in he es sequences was 1,621 and included a cumulaive oal of 1,175 wheelchair users. Each frame in he es sequences included a mos a single wheelchair user ha was ofen occluded by pedesrians around him/her. There were wo cases ha wheelchair users exised in he iniial frame or enering he frame. Wheelchair users exiing he frame were also included. For each frame of he sequences, bounding boxes of wheelchair users were manually annoaed as ground-ruh for evaluaion. In case a wheelchair user was occluded, we annoaed a likely bounding box by considering he conex. 5.3 Model of Wheelchair Users In he experimen, we used a hree-componens DPM deecor for deecion. In raining of he DPM deecor, raining samples were divided ino hree clusers based on heir aspec raio, and he model of 170

7 Wheelchair-user Deecion Combined wih Pars-based Tracking Table 1: Deecion accuracy of wheelchair users by each mehod. Mehod DPM DPM + Full-body racking DPM + Muli-racker (Zhang e al., 2012) DPM + Pars-racker (Proposed mehod) (a) DPM Precision Crierion Recall F-measure (c) DPM + Muli-racker (b) DPM + Full-body (Zhang e al., 2012) racking Figure 10: Examples of deecions by each mehod. wheelchair users was consruced for each cluser. The number of pars each DPM model included was experimenally se o six. The raining resul for wheelchair users is visualized in Figure 9. In he experimen, he publicly available code of DPM published by Girshick e al. (Girshick e al., 2012) was used. 5.4 Resuls & Discussions Table 1 shows he resul of deecions from es sequences. This resul indicaes ha he proposed mehod is more accurae on recall and F-measure han oher comparaive mehods. From his resul, he effeciveness of he proposed mehod (DPM + Pars-racker)for deecion in a crowded scene can be confirmed. Noe ha he proposed mehod is less accurae in precision han DPM. This is because he failure of racking leads o an increase of false pos- (d) DPM + Pars-racker (Proposed mehod) iives. However, he proposed mehod achieved he highes precision of he mehods which used racking. This indicaes ha he proposed pars-based racking is more accurae han oher racking mehods. The deecion accuracy of DPM + Muli-racker is worse han DPM. Since DPM + Muli-racker used fullbody racking, he racking ofen failed when arges were occluded. In conras, he proposed mehod used pars-based racking, so i improved he deecion accuracy even if he arges were occluded. Figure 10 shows examples of deecions by he comparaive mehods and he proposed mehod. Each row of Figure 10 shows he deecion resul by each mehod in he same frame of he es sequences. In he figure of he proposed mehod, he inner small recangles indicaed by a broken line are prediced bounding boxes of pars wih low confidence. The oher inner recangles are bounding boxes of pars wih high confidence. The ouer large recangles are he esimaed 171

8 VISAPP Inernaional Conference on Compuer Vision Theory and Applicaions full-body bounding boxes of he arges. The resuls in he firs row indicae ha he arge which could no be deeced by he DPM deecor was successfully deeced by being combined wih racking. These resuls show he effeciveness of combining deecion wih racking. Moreover, he resuls in he second row and he hird row show ha he proposed mehod esimaed bounding boxes of wheelchair users more accuraely han oher comparaive mehods. The proposed pars-based racking could esimae he bounding boxes even if mos of he pars were occluded. These resuls show ha proposed pars racking is robus agains heavy occlusions and i can compensae false negaives of he deecor saisfacorily. 6 CONCLUSIONS In his paper, we proposed a mehod for deecing wheelchair users accuraely in a crowded scene. Deecion of wheelchair users was difficul when hey were occluded, bu he proposed mehod coped wih i by combining he deecor wih pars-based racking. To rack he pars of wheelchair users accuraely, he proposed mehod esimaed he posiion of pars wih low racking confidence based on heir rajecories and iner-pars posiional relaionships. Experimenal resuls showed ha he proposed mehod can deec hem in a crowded scene more accuraely han comparaive mehods. As fuure work, we will consider a more effecive score funcion in pars-based racking o furher improve locaing of pars wih low confidence. We will also modify he mehod for associaing he deecion resuls. In addiion, we will inroduce sophisicaed moion dynamics of wheelchair users. ACKNOWLEDGEMENTS Pars of his research were suppored by MEXT, Gran-in-Aid for Scienific Research. We would like o hank he members of he laboraory for heir cooperaion as subjecs for creaing he daase. Bolme, D. S., Draper, B. A., and Beveridge, J. R. (2009). Average of synheic exac filers. In Proceedings of he 22nd IEEE Compuer Sociey Conference on Compuer Vision and Paern Recogniion, pages Dalal, N. and Triggs, B. (2005). Hisograms of oriened gradiens for human deecion. In Proceedings of he 18h IEEE Compuer Sociey Conference on Compuer Vision and Paern Recogniion, volume 1, pages Felzenszwalb, P. F., Girshick, R. B., McAlleser, D., and Ramanan, D. (2010). Objec deecion wih discriminaively rained par based models. IEEE Transacions on Paern Analysis and Machine Inelligence, 32(9): Girshick, R. B., Felzenszwalb, P. F., and McAlleser, D. (2012). Discriminaively rained deformable par models, release 5. Available a: hp://people.cs.uchicago.edu/ rbg/laen-release5/ [Accessed 14 Sep. 2016]. Henriques, J. F., Caseiro, R., Marins, P., and Baisa, J. (2015). High-speed racking wih kernelized correlaion filers. IEEE Transacions on Paern Analysis and Machine Inelligence, 37(3): Huang, C.-R., Chen, C.-S., and Chung, P.-C. (2006). Conras conex hisogram A discriminaing local descripor for image maching. In Proceedings of he 18h IEEE Inernaional Conference on Paern Recogniion, volume 4, pages Huang, C.-R., Chung, P.-C., Lin, K.-W., and Tseng, S.-C. (2010). Wheelchair deecion using cascaded decision ree. IEEE Transacions on Informaion Technology in Biomedicine, 14(2): Myles, A., Lobo, N. D. V., and Shah, M. (2002). Wheelchair deecion in a calibraed environmen. In Proceedings of he 5h Asian Conference on Compuer Vision, pages Pan, J. and Hu, B. (2007). Robus occlusion handling in objec racking. In Proceedings of he 20h IEEE Compuer Sociey Conference on Compuer Vision and Paern Recogniion, pages 1 8. Zhang, J., Presi, L. L., and Sclaroff, S. (2012). Online muli-person racking by racker hierarchy. In Proceedings of he 9h IEEE Inernaional Conference on Advanced Video and Signal-Based Surveillance, pages Zhang, J., Presi, L. L., and Sclaroff, S. (2013). Online muli-person racking by racker hierarchy. Available a: hp://cspeople.bu.edu/jmzhang/racker hierarchy/ Tracker Hierarchy.hm [Accessed 14 Sep. 2016]. REFERENCES Bolme, D. S., Beveridge, J. R., Draper, B., and Lui, Y. M. (2010). Visual objec racking using adapive correlaion filers. In Proceedings of he 23rd IEEE Compuer Sociey Conference on Compuer Vision and Paern Recogniion, pages