Enhancement of Particle Filter Approach for Vehicle Tracking via Adaptive Resampling Algorithm

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1 20 Thrd Inernaonal Conference on Compaonal Inellgence, Commncaon Sysems and eorks Enhancemen of Parcle Fler Approach for Vehcle Trackng va Adapve Resamplng Algorhm We Leong Khong We Yeang Ko Farrah Wong Ismal Saad Kenneh Tze Kn Teo 2 Modelng, Smlaon and Compaonal Algorhm Laboraory School of Engneerng and Informaon Technology Unvers Malaysa Sabah Koa Knabal, Malaysa eleongkhong@eee.org kkeo@eee.org 2 Absrac oadays, vehcle rackng s a val approach o asss and mprove he road raffc conrol, srvellance and secry sysems by havng he deal of he capred vehcle nformaon. In pas, many rackng echnqes have been mplemened and sffered from he ell knon occlson problems. Increasng he accracy of he rackng algorhm has cased he compaonal cos de o he nflexbly o adap he paral and flly occlded saons. Besdes occlson, appearance of ne objecs and backgrond noses n he capred vdeos ncrease he dffcles of connosly rackng he labelled vehcles. In hs paper, an adapve parcle fler approach has been proposed as he rackng algorhm o solve he vehcle occlson problem. In order o solve he common parcle fler degeneracy problem, he proposed parcle fler s eqpped h he adapve resamplng algorhm hch s capable of dealng h varos occlson ncdens. The expermenal resls sho ha enhancemen of he parcle fler va resamplng algorhm has been robsly rackng he vehcles, and sgnfcanly mprove he accracy n rackng he occlded vehcles ho compromsng he processng me. Keyords - Vehcle rackng; Parcle fler; Lkelhood; Resamplng I. ITRODUCTIO In recen mes, he nmber of he on-road vehcles has been obvosly ncreasng. As he sers of he vehcle ncreases, he ncden sch as accden ha creaed by he vehcle ser are also elevaed. Therefore, he demand of he raffc monorng sysem has nced researchers o sdy he vehcle rackng mehod n order o develop a complee sysem of road raffc conrol, srvellance and secry by rackng he vehcle nformaon. Today, many places have mplemened vdeo camera nsead of sng sensor for raffc monorng prpose de o he developmen of vdeo srvellance nfrasrcre. Many ype of vehcle nformaon sch as velocy of he vehcle, lcense plae of he vehcle, knd of he vehcle and vehcle flo can be easly obaned hrogh mage processng mehod. Hence, vehcle rackng has been an acve feld of research n order o mprove he road raffc conrol, srvellance and secry sysem. There are varos echnqes developed for vehcle rackng prpose. For example, Kalman fler [, 2], opcal flo, Markov Chan Mone Carlo are he ell knon mehod been sng noadays. Parcle fler s a poerfl echnqe n dealng h he non-lnear and non-gassan problems [3]. Therefore, s chosen as he sdy echnqe for vehcle rackng n hs paper. Drng he vsal rackng process, here mgh be some of he eak or lo egh parcle hch ll block he frher mprovemen of he algorhm. Ths phenomenon s addressed as parcle degeneracy. Parcle degeneracy s he essenal problem faced hen parcle fler appled on vsal rackng [4]. Therefore, resamplng s he solon o overcome he parcle degeneracy problem by elmnang he eak parcle and regenerae a ne se of srong egh parcle [5]. Hence, resamplng s an mporan sep for a parcle fler algorhm o ncrease he accracy of he vehcle rackng. Snce he raffc flo noadays s geng saraed, he vehcle mgh be occlded by oher objec from he vdeo capred by he srvellance sysem. Therefore, n order o mprove he applcably of parcle flerng n vehcle rackng, s algorhm ms be robs o parally or flly occlson. Occlson s a dffcl ask for he connosly vehcle rackng prpose [6]. When he arge objec s beng occlded, he daa of he arge objec ll be los. Therefore, more resamplng sep ll be needed o oban he vehcle nformaon. Sbseqenly, f he vehcle nformaon faled o recover afer several repeaed resamplng seps, he vsal racker s gong o lose rack on he arge objec. Therefore, an adapve resamplng algorhm s nrodced no parcle fler o cenly gan back he nformaon of he arge vehcle and connosly rack he vehcle nder varos occlson ncdens. The paper s organzed as follos. In secon II, he descrpon of parcle fler s brefly nrodced. In he follong secon, he mehod of obanng he color dsrbon model ll be presened. In secon IV, a Bhaacharyya cocen mehod s nrodced n order o calclae he lkelhood. Secon V shos he mehod of localzng. Secon VI dscsses he case of parcle degeneracy and resamplng algorhm, hle secon VII presens and dscsses he resl obaned from he mehods. A he end of he paper, conclsons are presened / $ IEEE DOI 0.09/CICSy

2 II. PARTICLE FILTER Parcle Fler also knon as seqenal Mone Carlo hch s an mporan Bayesan seqenal samplng echnqe [7]. I s recrsvely approxmang he poseror dsrbon sng eghed samples. Parcle fler normally consss of hree mporan seps. Frs sep s o generae ne parcles h each parcle represens he esmaed poseror poson. The accracy of he esmaon s mprovng h he ncreasng of he nmber of parcles. Unfornaely ncreasng he parcle sample sze h more parcles cased he compaonal me o be longer. Second sep s o compe each parcle egh based on lkelhood. In hs sdy, he color lkelhood ll be sed as he parameer for vehcle rackng. The parcle ll be eghng based on he smlary of he color hsogram of he reference arge h he sample arge. The more smlar of he color hsogram, he parcle ll be assgnng more egh. Thrd sep refer o he resamplng par. In common, parcle degeneracy s he man problem faced hen parcle fler appled o vsal rackng. The resamplng par s able o elmnae he eak parcle and regenerae he parcle h he larges egh so ha more accracy of he rackng resl can be obaned. Generally, parcle flerng s nrodced o rack objecs n hch he poseror densy p ( X Z ) and he observaon densy p ( Z X ) are ofen non-gassan. The vecor X refer o he qanes of racked objec hle Z denoes he observaons p o me. Therefore, a me, he sae can be pdaed sng Bayes rle n () Z X ) P( X Z: ) p ( X Z: ) () Z Z ) : The poseror X Z : ) s esmaed by a fne se of samples h mporan eghs. The eghs of he samples sho n (2) z x ) x x q( x x, z ) (2) : : III. COLOR DISTRIBUTIO MODEL In hs research, color has been chosen as he parameer for vehcle rackng. Color s an mporan parameer ha can be sed for rackng objecs ha are paral occlson. Besdes ha, he color of he vehcle s easer o be deeced and he processng me o oban he color nformaon s mch faser han oher parameers. The nformaon of he arge model can be obaned by generang he color hsogram. The hsogram s normally calclaed n he RGB color space sng 8 x 8 x 8 bns old be a dscree hsogram. Afer obanng he color hsogram of he sample, ll be comparng h he color hsogram of he ) reference n order o calclae he smlary of he o hsograms. A poplar mehod ha sed for measrng he smlary of o dsrbons s Bhaacharyya cocen. Bhaacharyya cocen s sed o deermne he lkelhood and calclae he eghng of he respecve parcle samples n hs sdy. IV. BHATTACHARYYA COEFFICIET Bhaacharyya cocen s a poplar measremen beeen o color dsrbons [8]. I defnes a normalzed dsance among color hsogram of references, ) and color hsogram of samples, q() as n (3). ρ [ p, q] p q d (3) Snce, he color hsogram s a dscree densy. Therefore, he cocen s defned as n (4), c ρ [ p, q] p q (4) p p... here { } c and { } c. The larger he cocen ρ represens he more smlares of he color dsrbons beeen he reference and arge color dsrbon. For o dencal normalzed color hsograms, he Bhaacharyya cocen, ρ. q q... V. TARGET LOCALIZATIO Afer eghng he enre parcle by he lkelhood, he eak parcle or he lo egh parcle ll be elmnaed and resample a ne se of sample o replace hose naned parcle o avod he parcle degeneracy problem occr. Afer resamplng, he parcle ll be dsrbed more concenrae and focs o he cener of he arge. Hence, n hs parcle fler algorhm, he locaon of he vehcle can be easly localzed hrogh he mean coordnaes generaed by he parcles of he arge n he area of neres. VI. RESAMPLIG ALGORITHM A. Parcle Degeneracy Parcle fler s a poerfl and proven algorhm ha sed for objec rackng for non-gassan and non-lnear dsrbon. I consss of hree basc sages hch are nalzaon, predcon, and pdang o form a sngle eraon of he recrsve algorhm. Hoever, afer a fe eraons, he parcle degeneracy ll occr and canno be avoded. Therefore resamplng s needed o overcome he degeneracy problem. In order o measre he parcle degeneracy, calclaon of he ecve sample sze s shon n (5). s (5) * + Var( ) 260

3 here * x z: ) q( x x, z ) s referred o he re egh. In hs case, he re egh canno be evalaed, and hence an esmaon of need o be generaed as n (6), s ( ) 2 here s he normalzed egh as n (2). Therefore, based on (5), can be noced ha s ndcaes parcle degeneracy ll occr. Alhogh a very large sample sze mgh mprove he accracy of he parcle fler rackng, s compaonal cos ll be very expensve. Therefore, he mos cen and common ay o solve hs degeneracy phenomenon s o adap he resamplng process. B. Tradonal Resamplng Resamplng s one of he ays o solve he parcle degeneracy problem. Therefore, many researchers have done research on dfferen resamplng algorhm. For example, mlnomal resamplng, resde resamplng, sysemac resamplng and srafed resamplng are among he mos common resamplng algorhm ha sed o redce he parcle degeneracy problem. Table I shos he radonal resamplng algorhm ha normally sed o redce he degeneracy problem. TABLE I. TRADITIOAL RESAMPLIG ALGORITHM Tradonal Resamplng Algorhm [{ * x, } ] [{, } RESAMPLE x ] Calclae he, s 2 ( ) IF < Thres Resample he dscree dsrbon { :,..., } Generae ne se of parcles { x :,..., } Weghng he parcles { x x } { ED IF x x,,..., } (6) In Table I, s he esmaon of ecve sample sze hch s sed o deermne he degeneracy of he parcle egh. When degeneracy occrred drng he rackng process, resamplng sage ll be acvaed o regenerae a ne se of samples and eghng he ne se of parcle agan sng he color lkelhood echnqe. If he ne se of he parcle s no accepable, resamplng sage s connally evalaed nl he esmaon of ecve sample sze has passed he hreshold vale. Therefore, he resamplng process mgh consme heavy compaonal me. Besdes ha, hen he vehcle s beng occlded, he nformaon of he vehcle ll be los. Therefore, more eraon of resamplng algorhm needs o be execed n order o ge a more accrae resl. More me s reqred o resample de o he dssmlary color hsogram of he reference h he color hsogram of he arge. C. Improved Resamplng An mproved parcle fler approach h he adapve fncon of resamplng process s examned n hs sdy. I s beleved ha h he adapve fncon o he varos occlded condons, resamplng processng me ll be opmzed n objec rackng. In hs sdy, only he eakes and loes egh parcles ll be elmnaed and TABLE II. IMPROVED RESAMPLIG ALGORITHM Improved Resamplng Algorhm [{ * x, } ] [{, } RESAMPLE x ] Calclae he, s 2 ( ) IF < Thres Choose he hghes egh of he parcle IF < ED IF { ED IF hres Elmnae he eak parcle M M + Generae remanng of parcles { x :,..., M} Weghng he parcles { x x } x x,,..., } 26

4 resampled. Meanhle, he parcle h he acceped egh ll be sorng for rackng prpose. The prpose of hs s o faser he resamplng process. In hs case, s more sable o be sed hen he vehcle s no occlded. Moreover, he resamplng sage has been shorenng. Hoever, hen he vehcle s occlded, here mgh be rong nformaon obaned by he vsal racker de o lack of resamplng process. Ths s anoher challengng par n vsal rackng here rong nformaon may case major problem afer he occlson. Therefore, hen occlson s occr, only he larges egh of parcle s reserved for resamplng prpose. Table II shos he mproved resamplng algorhm for vehcle rackng. VII. RESULT AD DISCUSSIO In hs secon, he resl of vehcle rackng sng radonal resamplng (Fg. ) ll be compared o he resl of vehcle rackng sng an mproved resamplng algorhm (Fg. 2). In boh cases, he parcle sze as nalzed as 200 parcles. Color hsogram as seleced o be sed n he vehcle rackng algorhm. Varos color hsograms ere comped n he RGB color space sng 8 x 8 x 8 bns. In hs paper, color as chosen as he rackng parameer becase color s more easly o be denfed as he vehcle deny hen paral occlson occrs. Color hsogram consss of dscree daa. Therefore s faser o process he daa of he color. The famos mehod sed o calclae he lkelhood of he color hsogram s Bhaacharyya cocen. The more smlar of he color hsogram, he Bhaacharyya cocen ll rern hgher vale h maxmm vale of one. As shon n Fg. and Fg. 2, he crossng con as represened he parcle dsrbon. Meanhle he sold box as ndcaed he locaon of he arge vehcle. The arge localzaon as deermned by he mean vale of he esmaed poson of he parcle. The mean vale as seleced as he coordnaon of arge becase he parcle has been normalzed. Base on he resls shon n Fg. and Fg. 2, he rackng as dvded no for cases, namely he vehcle before occlded, parally occlded, flly occlded and afer occlded. Referred o he seqence of resls shon n Fg. and Fg. 2, he adapve parcles fler resamplng shon he mch more promsng resl. The arge vehcle as sccessflly beng racked hrogho he vdeo alhogh as flly occlded a Frame 32. In case, he movng vehcle occrred before he occlson as shon n Frame 26 of Fg. and Fg. 2. The radonal and mproved parcle fler resamplng algorhm as able o rack he movng vehcle. In hs case, he parcle as resampled as sal by elmnaed he eak and lo egh parcles and resampled he parcle o replace hose elmnaed parcle. In case 2, he movng vehcle as parally occlded as shon n Frame 29 of Fg. and Fg. 2. In hs case, shoed ha color s a sefl parameer ha cold be sed Frame 26 Frame 29 Frame 32 Frame 35 Frame 38 Fgre. Resl of vehcle rackng by sng radonal resamplng parcle fler. Frame 26 Frame 29 Frame 32 Frame 35 Frame 38 Fgre 2. Resl of vehcle rackng by sng adapve parcle fler resamplng algorhm. 262

5 for parally occlson. When here as a dfferen color beeen he o objecs, color as he promsng parameer for vehcle rackng prpose. In hs case, he resamplng algorhm sll acs as sal as nformaon of he vehcle sll obanable. Therefore, he boh he algorhms ere able o rack he vehcle. In case 3, he movng vehcle as almos flly occlded by anoher vehcle as shon n Frame 32 of Fg. and Fg. 2. Majory of he nformaon of he movng vehcle as los. The radonal resamplng algorhm faled o allocae he movng occlded vehcle h a grop of heavy parcles hch as rapped a anoher vehcle. Meanhle, he adapve resamplng algorhm seleced he specfc locaon h mos smlar color o he movng vehcle and resamplng hn ha area. Therefore, he adapve resamplng as able o rack he movng vehcle alhogh a small poron of he movng vehcle color as denfed. In case 4, he movng vehcle occrred afer beng occlded as shon n Frame 35 of Fg., he resl shoed ha he radonal resamplng algorhm as nable o rack he labeled movng vehcle. The radonal resamplng mehod faled o rack he vehcle becase hen he movng vehcle as occlded, he nformaon of he vehcle as los and replaced by he color hsogram of oher vehcle. Therefore, he movng vehcle los rack by he vsal racker as shon n Frame 38 of Fg.. On he oher hand, he adapable parcle resamplng algorhm as able o keep rack he labeled movng vehcle as shon n Frame 35 of Fg. 2. Afer occlson occrs, he lkelhood of he arge vehcle decreased as compared o he reference hsogram. The mproved parcle fler elmnaed he enre parcles excep he parcles h he hghes egh. Based on he locaon of he remanng hghes eghed parcles, a ne se of sample ll be resampled. Therefore, he movng vehcle as able o be racked by he vsal racker as shon n Frame 38 of Fg. 2. VIII. COCLUSIO In hs paper, an adapve resamplng parcle fler algorhm has been proposed for vehcle rackng prpose. As dscssed n he prevos secon, parcle degeneracy dmnshes he accracy of he rackng algorhm. In order o sffcenly avod he parcle degeneracy, resamplng s an mporan solon o mprove he performance of he rackng sysem. Alhogh more parcles are nclded no he rackng sysem may ncrease he rackng accracy, b he compaon cos ll be ncreased heavly. Wh he adapable parcle fler resamplng algorhm, s capably of dealng h varos occlson ncdens as shon n he resls. The expermenal resls shoed ha enhancemen of he parcle fler va resamplng algorhm had been robsly rackng he vehcles, and sgnfcanly mproved he accracy n rackng he occlded vehcles ho compromsng he compaonal me. ACKOWLEDGEMET The ahors old lke o acknoledge he fndng asssance of he Mnsry of Hgher Edcaon of Malaysa (MoHE) nder Fndamenal Research Gran Schemes (FRGS), gran o. FRG0220-TK-/200 and Unversy Posgradae Research Scholarshp Scheme (PGD) by Mnsry of Scence, Technology and Innovaon of Malaysa (MOSTI). REFERECES [] P.L.M. Boefroy, A.Bozerdom, S.L. Phng, A. Beghdad, Vehcle Trackng sng Projecve Parcle Fler, IEEE, 2009, DOI 0.09/AVSS [2] L Jng, Prahlad Vadakkepa, Ineracng MCMC Parcle Fler for Trackng Maneverng Targe,Dgal Sgnal Processng 20 (200) [3] M. Sanjeev Arlampalam, Smon Maskell, el Gordon, Tm Clapp, A Toral on Parcle Fler for Onlne onlnear/on-gassan Bayesan Trackng, IEEE Transacon on Sgnal Processng, Vol. 50, o.2, Febrary 2002 [4] T.Wada, F.Hang, and S.Ln, Vsal Trackng Usng Parcle Flers h Gassan Progress Regresson, Sprnger-Verlah Berln Hedelberg PSIVT 2009, LCS 544, pp , 2009 [5] Xaoyan F, Yngmn Ja, An Improvemen on Resamplng Algorhm of Parcle Fler, IEEE Transacon on Sgnal Processng, Vol. 58, o.0, Ocober 200 [6] Andre D. Bagdanov, Alber Del Bmbo, Fabrzo Dn, Waler mza, Adapve Uncerany Esmaon for Parcle Fler- based Trackers, 4 h Inernaonal Conference on Image Analyss And Processng (ICIAP2007), IEEE.. [7] Tao Zhang, Shmn Fe, Xaodong L, Hong L, An Improved Parcle Fler for Trackng Color Objec, IEEE 2008, DOI 0.09/ICICTA [8] M.Sohal Khald, M.Umar Ilyas, M. Saqb Sarfaraz, M.Azm Ajaz, Bhaacharyya Cocen n Correlaon of Gray-Scale Objecs, Jornal of Mlmeda, Vol. o., APRIL