PARTICLE FILTER BASED VEHICLE TRACKING APPROACH WITH IMPROVED RESAMPLING STAGE

Transcription

1 ISS: (OLIE) ICTACT JOURAL O IMAGE AD VIDEO PROCESSIG, FEBRUARY 014, VOLUME: 04, ISSUE: 03 PARTICLE FILTER BASED VEHICLE TRACKIG APPROACH WITH IMPROVED RESAMPLIG STAGE We Leong Khong 1, We Yeang Kow 1, Ismal Saad 1, Chung Fan Lau 1 and Kenneh Tze Kn Teo Modellng, Smulaon & Compung Laboraory, Maeral & Mneral Research Un School of Engneerng and Informaon Technology, Unvers Malaysa Sabah, Malaysa Emal: 1 msclab@ums.edu.my, kkeo@eee.org Absrac Opcal sensors based vehcle rackng can be wdely mplemened n raffc survellance and flow conrol. The vas developmen of vdeo survellance nfrasrucure n recen years has drawn he curren research focus owards vehcle rackng usng hgh-end and low cos opcal sensors. However, rackng vehcles va such sensors could be challengng due o he hgh probably of changng vehcle appearance and llumnaon, besdes he occluson and overlappng ncdens. Parcle fler has been proven as an approach whch can overcome nonlnear and non-gaussan suaons caused by cluered background and occluson ncdens. Unforunaely, convenonal parcle fler approach encouners parcle degeneracy especally durng and afer he occluson. Parcle fler wh samplng mporan resamplng (SIR) s an mporan sep o overcome he drawback of parcle fler, bu SIR faced he problem of sample mpovershmen when heavy parcles are sascally seleced many mes. In hs work, genec algorhm has been proposed o be mplemened n he parcle fler resamplng sage, where he esmaed poson can converge faser o h he real poson of arge vehcle under varous occluson ncdens. The expermenal resuls show ha he mproved parcle fler wh genec algorhm resamplng mehod manages o ncrease he rackng accuracy and meanwhle reduce he parcle sample sze n he resamplng sage. Keywords: Vehcle Trackng, Parcle Fler, Genec Algorhm, Resamplng, Occluson 1. ITRODUCTIO Recenly, he number of on-road vehcles has been ncreased sgnfcanly and caused severe raffc congeson problem. Traffc congeson comprses complex dynamcs problem and nvolves many raffc parameers ha nerac wh one anoher [1]. Moreover, he on-road ncdens ha are creaed by he drvers are also elevaed. Hence, a wde range of vehcle rackng applcaons such as raffc survellance, nellgen drver asssan sysem (IDAS) and navgaon sysem have nced he researchers o rack vehcle wh mcroscopc modellng. Vehcle rackng sysem consss of hardware and sofware. Hardware s he devce aached o he vehcle or nsalled on road o oban he npu nformaon. Sofware s used o process he npu nformaon exraced from he hardware. Generally, hardware mplemened n he vehcle rackng sysem can be caegorzed no wo ypes, whch are acve sensors and passve sensors []. The acve sensors measure he dsance hrough he ravel me of a sgnal emed by he sensors and refleced from he nearby vehcle. However, he acve sensors have drawbacks of low spaal resoluon and slow scannng speed. When a huge amoun of vehcles are movng smulaneously, he acve sensors ofen oban wrong sgnal [3]. On he conrary, he passve sensors such as vdeo cameras can provde a wde range of nformaon o characerze he vehcle. For example, he vehcle feaures such as colour, edge and shape can be obaned by exracng he nformaon from he vdeo camera va mage processng echnques. Due o he low cos of passve sensors, hey are more cos-cen o be mplemened n vehcle rackng sysem as compared o he acve sensors. In many counres, vdeo camera has been mouned on he pole near o roadsde for capurng he raffc scene. As he hegh of pole s consraned, he camera has he low angle vew. Hence, occluson mgh be frequenly occurred. The raffc congeson worsens he occluson ncdens. Vehcle rackng n occluson scene s a challengng ask, because he feaures used o characerze he arge vehcle would nfluenced by he obsacles, whch creaes he nonlnear and non-gaussan suaons. Thus, he complexes and dffcules caused by he occluson ncdens become he research s drvng force o develop an ecve and cen vehcle rackng algorhm. Parcle fler has been proven as an approach o deal wh nonlnear and non-gaussan suaons. Parcle fler has been chosen n hs sudy o rack he vehcles under varous occluson ncdens. Parcle fler has faced he parcle degeneracy problem, as he varance of he mporance weghs ncreases hereby he algorhm faled o evade he wegh of degradaon. Formerly, parcle degeneracy can be mgaed by usng huge sample parcles or resamplng approach. Alhough he resamplng approach s more cen o solve parcle degeneracy compared wh he mplemenaon of huge sample parcles, he convenonal resamplng echnque such as samplng mporan resamplng (SIR) has creaed anoher praccal problem known as he sample mpovershmen. Therefore, an mproved parcle fler wh genec algorhm resamplng echnque has been mplemened n hs sudy o rack he arge vehcle under occluson ncdens. The expermenal resuls show ha genec algorhm based parcle fler resamplng can esmae he poson of he arge vehcle more accuraely.. LITERATURE REVIEW Vehcle rackng s usually performed n he conex where he hgher level of applcaons requres he vehcle poson n every consecuve frame. In general, vehcle rackng s a challengng assgnmen whn he feld of machne vson. The dffcules n rackng vehcles may arse due o abrup vehcle moon, occluson ncdens and changng appearance of paerns for boh he vehcles and envronmen. In he pas, dfferen rackng approaches have been mplemened such as Kalman fler, opcal flow, Markov Chan Mone Carlo, and parcle fler. In fac, each rackng approach 75

2 WEI LEOG KHOG e.al. : PARTICLE FILTER BASED VEHICLE TRACKIG APPROACH WITH IMPROVED RESAMPLIG STAGE has s own srenghs and weaknesses. For nsance, n research [4], he researchers saed ha Kalman fler s performed as an esmaor ha esmaes and correcs he sae of lnear process. Kalman fler s normally used o solve he problem wh lnear suaons because s a framework ha esmaes he arge sae and updaes he sae esmaon drecly based on he obaned measuremen. However, Kalman fler wll face he dffcules when dealng wh nonlnear suaons. Accordng o research [5], an exended verson of Kalman fler wll be requred n order o solve nonlnear suaons. The exended Kalman fler s mplemened o change he esmaed curren measuremen from nonlnear o lnear. everheless, when he nonlneary s naccuraely approxmaed, he esmaed rackng resuls for he exended verson of Kalman fler wll be dverged and affeced he rackng resuls. Opcal flow s anoher echnque used o rack he movng vehcle. I has been used due o s ably o reflec he moon feld of he capured mage. For nsance, he opcal flow has been used o deec and rack he movng objec n research [6]. However, opcal flow mehod faces dffcules when he movng arge became sac. Opcal flow faled o perform when overlappng ncdens occurred. Ths s due o he dffculy level of separang he arge objec wh he obsacles. When overlappng ncdens occurred, he opcal flow wll denfy he overlapped objecs wh he obsacles as a sngle blob and could easly rack o he wrong arge. Markov Chan Mone Carlo s also a common echnque ha s wdely mplemened for vehcle rackng purpose. Accordng o research [7], he dffculy of Markov Chan Mone Carlo s how o deermne he approprae amoun of sample sze for performng good qualy esmaon. For nsance, wh he huge number of samples, he complexy of compuaon wll become heavy and lead o unnecessary processng me. Meanwhle, leas amoun of sample could lead o nadequae rackng resuls. Alhough he developed algorhm was able o rack he movng arge vehcle wh adapve sample sze, he algorhm s sll unseady and consss of rackng errors n movng, overlapped arge vehcle a he capured frame. In hs sudy, parcle fler has been chosen o rack he arge vehcle under varous occluson ncdens. ormally, overlappng suaons wll creae sae unceranes and lead o non-lnear suaons. Accordng o research [8, 9], parcle fler s a powerful and promsng echnque ha can overcome he non-lnear suaons. By referrng o research [10], he convenonal parcle fler wll face parcle degeneracy hroughou he rackng process. Parcle degeneracy problems occurred when rackng process undergoes several eraons and blocks he furher mprovemen of he rackng algorhm. Research [11] saed ha he parcle degeneracy problem can be resolved eher by mplemenng a huge amoun of sample parcles or resamplng he parcles ha were elmnaed. Snce huge amoun of nalze sample parcles s always unfeasble due o he compuaonal complexy, resamplng he parcles becomes he mos approprae soluon o overcome he parcle degeneracy problem [1]. In research [11, 13], he rackng algorhm was developed by usng colour feaure. From he resuls, he rackng algorhm wh colour feaure can rack he arge accuraely under varous occluson suaons bu he rackng algorhm has dffculy when he background colour s smlar o he colour of he arge. In research [14], had presened a rear vew vehcle deecon algorhm based on edge feaure. The developed algorhm faled o respond when he vehcle edges are unclear or durng he occurrence of occlusons. Hence, n order o rack he vehcle robusly or o dfferenae he arge vehcle wh he obsacles, a fuson of mulple feaures wll be requred because can provde more nformaon o descrbe he arge vehcle [15]. In research [16], showed ha he rackng algorhm wh fuson of mulple feaures wll provde beer rackng resuls even when he arge vehcle s undergong parally and fully occluson. 3. PARTICLE FILTER FRAMEWORK Parcle fler s also known as sequenal Mone Carlo algorhm. I s a mansream rackng approach used o represen he propagaon condonal densy dsrbuon when he observaon probably densy dsrbuons are n nonlnear and non-gaussan suaons hroughou he vehcle rackng process. Moreover, ulzes he sequenal esmaon of he probably dsrbuons. The man dea of parcle fler s o predc he poseror dsrbuon based on a fne se of random weghed sample parcles,. Each weghed parcle s drawn o represen he esmaed sae of he arge vehcle based on he poseror dsrbuon as shown n Eq.(1). x, w 1,,3 S,..., (1) where, x denoes he sae of he arge vehcle and w denoes he wegh ha assocaed o he parcle. In hs sudy, he wegh ha assocaed o he parcles wll be lmed from zero o one, w 0,1. Afer each parcles are assocaed wh he wegh, he wegh for all he parcles can be normalzed and summed up o one as shown n Eq.(). w 1 1 () In general, parcle fler approach works ou based on hree mporan sages whch are he predcon sage, measuremen sage and resamplng sage. In he predcon sage, he ranson sae of he vehcle model wll be generaed randomly and wll be represened by a se of parcles. In he measuremen sage, he parcles wll assgn wegh based on he calculaon of he feaures lkelhood. The more smlar he reference feaures wh he arge feaures, he more he parcles wll be assgned wh heavy wegh. On he oher hand, f he lkelhood compued s small, he parcles wll be assgned wh low wegh. Lasly, n he resamplng sage, he low wegh parcles wll be regeneraed n order o avod parcle degeneracy. In hs sudy, parcle fler has been developed o rack a sngle vehcle ha undergoes varous occluson ncdens. The occluson ncdens conss of dynamc changes whch wll cause he poseror probably densy funcon p X Z and he observaon probably densy funcon p Z X compued n he parcle fler algorhm are ofen nonlnear and non-gaussan. From he poseror probably densy funcon and observaon probably densy funcon, X denoes he sae space of he 76

3 ISS: (OLIE) ICTACT JOURAL O IMAGE AD VIDEO PROCESSIG, FEBRUARY 014, VOLUME: 04, ISSUE: 03 vehcle beng racked whereas Z denoes all he esmaon sae space. 3.1 PREDICTIO STAGE Predcon sage n he parcle fler s he prmary sage ha naes he sample parcles and randomly esmaes he poson of he arge vehcle. Each parcle represens he esmaed poson of he arge vehcle ndvdually. Hence, he rackng accuracy of he algorhm ha s nalzed wh huge sample parcles can be mproved because of he hgher probably o predc he arge vehcle real poson. However, mplemenng huge amoun of sample parcles wll cause heavy compuaon. Thus, he pror probably densy funcon can be compued based on Eq.(3), whch s he deermned pror probably densy funcon, hen he poseror probably densy funcon can be calculaed hrough he updaed sep by usng he Bayes rule as defned n Eq.(4). Afer he poson of he arge vehcle s predced, he parcle fler wll move on o he measuremen sage n order o compue he wegh for each parcle. p( X Z1 : 1) p( X X1) p( X1 Z1: 1) dx 1 (3) p( Z X ) p( X Z1: 1) p ( X Z1: ) (4) p( Z Z ) 3. MEASUREMET STAGE 1: 1 Afer each parcle has been assgned wh he esmaed poson of he arge vehcle, he feaures of he vehcle wll be exraced based on he esmaed poson. Varous feaures such as colour, edge, shape or exure can be used o characerze he arge vehcle. In hs sudy, colour and shape feaures wll be used o compue he lkelhood of he arge vehcle. Based on he exraced arge vehcle feaures, he wegh of each parcle s deermned by calculang he probably of lkelhood. The colour lkelhood can be compued by usng Bhaacharyya dsance, b ds [17]. Bhaacharyya dsance s a famous mehod ha correlaes he mages usng colour hsogram. In hs sudy, he HSV colour hsogram wll be used o represen he colour feaure of he vehcle. The Bhaacharyya dsance wll be used o defne a normalzed dsance among he colour hsogram of he arge vehcle and he colour hsogram of he reference vehcle. ormally, Bhaacharyya cocen s used o measure he connuous probably dsrbuon, as defned n Eq.(5). [ p, q] p q du (5) where, p u represens he colour hsogram of he arge vehcle whereas q u represens he colour hsogram of he reference vehcle. Snce he colour hsogram of he vehcle s formed n dscree densy arrangemen, he colour lkelhood beween he arge vehcle and he reference vehcle can be measured hrough Eq.(6). c u u [ p, q] p u q u (6) u 1 Afer obanng he Bhaacharyya cocen, wll be ransformed o Bhaacharyya dsance by usng he Eq.(7). b ds 1 [ p, q] (7) Based on he compued Bhaacharyya dsance, he colour lkelhood can be calculaed hrough Eq.(8). b ds e 1 c (8) The parameers, φ c and σ n Eq.(8) represen he wegh deermned from he colour lkelhood and he adjusable sandard devaon respecvely. Due o he srucural oulook of a vehcle, he shape feaure wll be mplemened n he rackng algorhm. The shape lkelhood s deermned by usng Hausdorff dsance, H ds [18]. Hausdorff dsance s a scalar measuremen of he dsance value beween wo ses of pons. In pracce, he wo se of pons can be obaned from he shape feaure of he reference vehcle, A and he shape feaure of he arge vehcle, B as shown n Eq.(9) and Eq.(10). A { a } 1,..., c (9) B { b } 1,..., c (10) From he wo ses of pons deermned, he Hausdorff dsance beween se A and se B can be calculaed by usng Eq.(11). H ds ( A, B) max[ h( A, B), h( B, A)] (11) where, h(a, B) s he dsance measure from pons n se A o pons n se B and h(b, A) s he dsance measure from pons n se B o pons n se A. Afer obanng he Hausdorff dsance, he wegh of he parcles can be generaed usng Eq.(1). H ds e 1 s (1) The value of Bhaacharyya dsance and Hausdorff dsance wll equal o one f he feaures of he arge vehcle s same wh he feaure of he reference vehcle. Boh he feaures lkelhood wll be fused ogeher o provde a more accurae rackng for he parcles wegh deermnaon, as shown n Eq.(13). w ) (1 )( ) (13) ( c s where, α s he wegh consan. In hs sudy, α = 0.5 whch means 50% of he wegh was compued by usng colour lkelhood and he remanng 50% of he wegh was compued based on he shape feaure. The wegh of parcle wll be updaed hrough Eq.(14) when feaures lkelhood s compued. p( Z X ) p( X X 1) w w 1 (14) q( X X, Z ) 1 Afer he wegh o he parcles s updaed, wll be normalzed, by usng Eq.(15), before he predcve poseror densy funcon s predced. The wegh of he parcles s compued n a dscree form. The poseror densy funcon can be represened by Eq.(16). 77

4 WEI LEOG KHOG e.al. : PARTICLE FILTER BASED VEHICLE TRACKIG APPROACH WITH IMPROVED RESAMPLIG STAGE W w w 1 (15) p( X Z1 : ) W ( X X ( )) (16) 1 When he predced poseror densy dsrbuon s obaned, he parcle fler wll deermne he fnal poson of he arge vehcle by calculang mean sae of he vehcle by usng Eq.(17). 3.3 RESAMPLIG STAGE 1 E( X ) S (17) p 1 Parcle fler faces an nheren problem whch s parcle degeneracy afer a few eraon of rackng algorhm performed. The parcle degeneracy occurred because one of he parcles wll experence neglgble wegh due o he ncreasng varance of he parcles mporan wegh n every consecuve processng frame. I can be concluded ha parcle degeneracy s unavodable bu can be resolved hrough resamplng approach. In he pas, he mos common resamplng approach was samplng mporan resamplng (SIR) whch s performed based on replacemen bass. In he SIR approach, he low wegh parcles wll be elmnaed meanwhle he heavy wegh parcles wll be preserved for he rackng purpose. Ths shows ha a new se of heavy weghs parcles wll be duplcaed o replace he elmnaed parcles. The occurrence of parcle degeneracy can be deermned by calculang he ecve sample sze, as shown n Eq.(18). (18) * 1Var( w ) w * In Eq.(18), he wegh denoes he rue wegh whch can be calculaed hrough Eq.(19). w * p( x z1: ) (19) q( x x, z ) 1 However, he rue wegh of he parcles s always hard o be compued and hence an esmaon of ecve sample sze can be deermned by usng he normalzed wegh, w as shown n Eq.(0). 1 s W 1 If he compued esmaon of ecve sample sze (0) small or hres, he parcle degeneracy problem occurred and resamplng sage s requred o mprove he predcon of he poseror densy dsrbuon. The process of esmaon wll be done when he soppng crera was fulflled. The parcle fler wh SIR resamplng sage s llusraed n Table.1. s 4. PROPOSED GEETIC ALGORITHM RESAMPLIG Alhough he SIR approach can be used o reduce he problem of parcle degeneracy, creaes anoher praccal problem whch s he sample mpovershmen. The sample mpovershmen occurred because he parcles wh heavy wegh are sascally beng seleced many mes hroughou he rackng process. Hence, he sae esmaed by he algorhm wll conan many repeaed poson and leads o he loss of dversy among he parcles. Afer a few eraons, he esmaed poson mgh collapse o a sngle poson whch he compued lkelhood s hgh and causes he algorhm unable o rack he arge vehcle connuously. By elmnang he parcle dversy problem, he genec algorhm wll be mplemened n he parcle fler resamplng sage. Genec algorhm consss of selecon, crossover and muaon sages. The developed genec algorhm based parcle fler resamplng algorhm s llusraed n Table SELECTIO STAGE Selecon sage s mporan for mprovng he qualy of he populaon by selecng ndvduals wh hgher qualy o generae offsprng soluons, whch are referrng o he esmaed poson of he arge vehcle. There are several mehods o selec he parens for crossover such as Roulee wheel selecon, Bolzmann selecon, ournamen selecon, rank selecon and seady sae selecon. In hs sudy, he rank selecon wll be used o rearrange he poson of he vehcles based on he compued parcles wegh. In he rank selecon approach, he mos heavy parcles wll be assgned wh hgher rank and meanwhle he lgh wegh parcles wll be assgned wh lower rank. Afer all he parcles are beng ranked accordngly, he algorhm wll randomly selec wo parcles as he parens. The parens here are referrng o he predced poson of he arge vehcle. Snce he heavy wegh parcles are beng assgned wh hgher rank, he chance of he heavy parcles beng seleced as he parens wll be hgher as compared o he parcles of lower ranks. 4. CROSSOVER STAGE Afer he selecon sage, genec algorhm wll undergo he crossover sage. Crossover s a process of akng more han one paren soluons and combnng her characerscs o produce new offsprng soluons. In leraure, here are several ypes of crossover echnques such as one-pon crossover, wo-pon crossover, unform crossover, heursc crossover and arhmec crossover. In hs sudy, arhmec crossover wll be seleced as he parcle fler resamplng approach. The advanage of he arhmec crossover s o produce a new soluon whch conans he characerscs of boh parens. The offsprng soluon are deermned based on Eq.(1) and Eq.(). C 1 P1 P(1 ) (1) C P P1 (1 ) () where, α s he wegh facor wh a lm of zero o one, P1 and P are he parens and C1 and C are he offsprng soluon. 78

5 ISS: (OLIE) ICTACT JOURAL O IMAGE AD VIDEO PROCESSIG, FEBRUARY 014, VOLUME: 04, ISSUE: 03 Table.1. The algorhm of parcle fler wh SIR resamplng 1:Inalze model feaures and sample sze : FOR FRAME = 1,,, 3: PREDICTIO: 4: FOR = 1,,, 5: Draw predced parcles from pror dynamcs 6: Compue he feaures based on esmaed poson 7: ED FOR 8: MEASUREMET & UPDATE: 9: Calculae he lkelhood 10: Compue he wegh of he parcle 11: ormalze he wegh, 1: Calculae 13: hres hres 14: RESAMPLIG: 1 ( w ) w w ( w ) Resamplng Accepance 15: Elmnae low wegh parcles 16: Repea Sep 3 o Sep 13 17: ED IF 18: LOCALIZATIO: 19: x, y) E( X ) ( Wegh facor s used o deermne he fracon or he percenage of he characersc from he parens soluons ha wll conrbue o he offsprng soluons. In hs sudy, 0.7 was se as he wegh facor whch means ha 70% of he characersc of frs paren and 30% of he characersc of he second paren o form he frs chldren soluon and nversely for he second chldren soluon. In he genec algorhm resamplng approach, all he low wegh parcles wll be elmnaed and replaced by he generaed chldren soluons. By usng he arhmec crossover, he poson of he arge vehcle can be predced accuraely. 4.3 MUTATIO STAGE Muaon sage s a process o manan he genec dversy from one generaon o he nex generaon. Muaon sage mus be performed afer he selecon and crossover sage because of s ably as a fnal checkng sae o recover he nformaon whch mgh be los durng he selecon and crossover process. Muaon s an mporan sage o preven he populaon sagnang a he opmal poson. In leraure, he muaon rae s suggesed o be se farly low and s defned by he user. Ths s o evade he loss of f soluons whch can affec he esmaed soluon. In hs sudy, he muaon rae was se as 1%. If he muaon rae was h durng he rackng process, a new offsprng soluon wll be generaed by addng he deermned sae of he arge vehcle wh a random varable whch s srcly lmed o he values beween zero o one. Table.. The algorhm of parcle fler wh genec algorhm resamplng 1: Inalze model feaures and sample sze : FOR FRAME = 1,,, 3: PREDICTIO: 4: FOR = 1,,, 5: Draw predced parcles from pror dynamcs 6: Compue he feaures based on esmaed poson 7: ED FOR 8: MEASUREMET & UPDATE: 9: Calculae he lkelhood 10: Compue he wegh of he parcle 11: ormalze he wegh, 1: Calculae 13: hres hres ( w ) w w ( w ) Resamplng Accepance 14: RESAMPLIG: 15: Elmnae low wegh parcles 16: Performed Rank Selecon 17: Arhmec Crossover 18: Generae muaon rae 19 IF muaon < 1% 0: X X rand(0,1 ) 1: ELSE : X X 3: ED IF 4: LOCALIZATIO: 5: x, y) E( X ) ( 5. RESULTS AD DISCUSSUIOS In hs secon, he resul of vehcle rackng usng SIR (Fg.1) was compared o he resuls of vehcle rackng usng genec algorhm resamplng (Fg.). The nal amoun of parcles used for vehcle rackng was se as 00 parcles. As shown n Fg.1 and Fg., he sold boundary box ndcaes he boundary of he vehcle meanwhle he cross con represens he predced poson of he arge vehcle. Snce parcle fler s consderng he poseror densy dsrbuon, he esmaed poson of he arge vehcle wll be deermned by calculang he mean value. By referrng o Fg.1 and Fg., he rackng process undergoes hree sages whch are: whou occluson, parally occluson and fully occluded. When he arge vehcle s free from occluson as shown a Frame 5 n Fg.1 and Fg., he arge vehcle has been accuraely racked by he parcle fler wh SIR resamplng and he mproved parcle fler wh genec algorhm. Ths s due o he colour and shape feaures beng exraced s easly compued no he lkelhood. Besdes, he nformaon of he arge vehcle s no nfluenced by he 79

6 WEI LEOG KHOG e.al. : PARTICLE FILTER BASED VEHICLE TRACKIG APPROACH WITH IMPROVED RESAMPLIG STAGE obsacle. Hence, heavy wegh parcles can be produced. The rackng cency for arge vehcle whch s free from occluson wll be always faser and easer. Comparng Frame 1 a Fg.1 and Fg., he arge vehcle s parally occluded by anoher movng vehcle. From he resuls, he parcle fler wh SIR resamplng merely locaes he arge vehcle meanwhle he parcle fler wh genec algorhm resamplng s able o locae he arge vehcle. Durng he parally occluson, he feaures ha are used o descrbe he arge vehcle s affeced by he obsacle vehcle. A hs sage, resamplng akes an mporan role o accuraely locae he arge vehcle. However, he SIR has duplcaed he parcles of heavy wegh o replace he amoun of he parcles ha has been elmnaed. Thus, he esmaed poson s easly rapped n a sngle locaon whch dverges from he real poson of he arge vehcle. In parcle fler wh genec algorhm resamplng, he elmnaed parcles wll be replaced by he recombnaon of wo esmaed poson. Afer recombnaon he parcles, he lkelhood wll be recalculaed. Afer a few eraons of recombnaon, he rackng wll be more accurae due o he convergence of he esmaed poson o he real poson of he arge vehcle. By referrng o Frame 33 n Fg.1 and Fg., he arge vehcle s fully occluded by he movng vehcle. A hs momen, he nformaon of he arge vehcle s fully los and s affeced by he movng vehcle. From he resuls, he parcle fler wh SIR s unable o locae he arge vehcle and meanwhle genec algorhm based parcle fler resamplng can sll predc he poson of he arge vehcle. A Frame 41 n Fg.1 and Fg., he arge vehcle reappeared from a full and paral occluson. From he resuls, he parcle fler wh genec algorhm has accuraely predced he posons of he arge vehcle. However, he parcle fler wh SIR merely racks he arge vehcle. Ths s because he parcle fler wh SIR hardly obaned nformaon of he arge vehcle. Comparng Frame 49 n Fg.1, and Fg., he arge vehcle reappears afer crcal full occluson. The rackng algorhm wll ry o rean he nformaon of he arge vehcle. The parcle fler wh SIR requres more compuaonal me o rack back he arge vehcle because he esmaed poson n SIR s based on he random generaed value wh a normal dsrbuon. However, parcle fler wh genec algorhm wll ry o recombne he good parcles n order o predc he new poson of he arge vehcle. Based on hs recombnaon and wegh recalculaon process, he real poson of he arge vehcle can be obaned. (c) Frame 33 (d) Frame 41 (e) Frame 49 Fg.1. Resul of vehcle rackng va samplng mporan resamplng (SIR) (a) Frame 5 (b) Frame 1 (c) Frame 33 (d) Frame 41 (e) Frame 49 (a) Frame 5 (b) Frame 1 Fg.. Resul of vehcle rackng va genec algorhm resamplng In order o nvesgae he rackng performance of he parcle fler wh SIR and genec algorhm, he roo mean square error (RMSE) s compued. The RMSE of he parcle fler wh SIR and he developed genec algorhm s ploed n 730

7 Resamplng Parcle Sze Resamplng Coun RMSE ISS: (OLIE) ICTACT JOURAL O IMAGE AD VIDEO PROCESSIG, FEBRUARY 014, VOLUME: 04, ISSUE: 03 Fg.3. The lower value of RMSE shows a beer esmaon of he posons of he arge vehcle. From he resuls shown n Fg.3, s clearly shown ha he RMSE for he parcle fler wh genec algorhm resamplng s much lower han he SIR approach. Thus, can be concluded ha he parcle fler wh genec algorhm resamplng have a beer esmaon resuls han he parcle fler wh SIR sage hroughou he rackng process. The comparson of he resamplng coun and parcle sze ha are requred n he resamplng sage beween SIR and genec algorhm resamplng s ploed n Fg.4. From he resuls, he number of parcles ha are requred for he genec algorhm resamplng s much lesser han he SIR approach hroughou he whole vehcle rackng process. Ths s because genec algorhm has he ably o converge he esmaed poson o he real poson of he arge vehcle hrough he recombnaon process. However, n SIR, he poson of he arge vehcle s generaed based on random predcon. Due o he beer esmaon resuls obaned hrough genec algorhm, he parcles ha requred resamplng are grealy reduced. For nsance, when he arge vehcle s parally occluded durng Frame 1, he number of parcles ha are requred for resamplng n SIR approach s 1340 and he parcles ha are requred for genec algorhm resamplng s 775. Hence, he mproved parcle fler wh genec algorhm resamplng has reduced around 4.% of he parcles amoun compared o he SIR approach. Besdes, he beer esmaon resuls obaned by he genec algorhm also reduce he number of resamplng eraon. Hence, can be concluded ha he mproved resamplng provdes a beer rackng accuracy under varous occluson ncdens wh he leas amoun of parcles mplemened RMSE VS FRAME IDEX SIR (00 Parcles) Genec Algorhm (00 Parcles) Frame Index Fg.3. Graph of RMSE vs frame ndex for SIR resamplng and genec algorhm resamplng Resamplng Coun & Resamplng Parcle Sze VS Frame Index COCLUSIO As dscussed earler, parcle degeneracy could dmnsh he accuracy of he parcle fler approach. Thus, resamplng sage plays an mporan par n he parcle fler algorhm n order o rack vehcle accuraely under varous occluson ncdens. The mos common resamplng approach s SIR. However, wll face he sample mpovershmen especally durng he suaons ha conans hgh uncerany. The mplemenaon of genec algorhm n he parcle fler resamplng approach s capable o allevae he rackng dffcules under varous occluson suaons by recombnng and recalculang he lkelhood of he parcles. From he resuls, he performance and robusness of he developed resamplng algorhm s promsng. Based on he beer esmaon of he genec algorhm, can be concluded ha he developed vehcle rackng algorhm has mproved he accuracy of he rackng resuls. The number of parcles ha are requred for resamplng has also been reduced compared o he SIR approach Frame Index SIR [00 Inal Parcles] (Resamplng Parcle Sze) Genec Algorhm [00 Inal Parcles] (Resamplng Parcle Sze) SIR [00 Inal Parcles] (Resamplng Coun) Genec Algorhm [00 Inal Parcles] (Resamplng Coun) Fg.4. Resamplng coun and resamplng parcle sze versus frame ndex for samplng mporan resamplng (SIR) and genec algorhm based parcle fler resamplng ACKOWLEDGEMET The auhors would lke o acknowledge he fnancal asssance from Mnsry of Hgher Educaon of Malaysa (MOHE) under Exploraory Research Gran Scheme (ERGS), gran no. ERG001-TK-1/01, Unvers Malaysa Sabah (UMS) under UMS Research Gran Scheme (SGPUMS), gran no. SBK006-TK-1/01, and he Unversy Posgraduae Research Scholarshp Scheme (PGD) by Mnsry of Scence, Technology and Innovaon of Malaysa (MOSTI)

8 WEI LEOG KHOG e.al. : PARTICLE FILTER BASED VEHICLE TRACKIG APPROACH WITH IMPROVED RESAMPLIG STAGE REFERECES [1] G. Papageorgou, P. Damanou, A. Pslldes, T. Aphams, D. Charalambous and P. Ioannou, Modellng and Smulaon of Transporaon Sysems: a Scenaro Plannng Approach, Journal for Conrol, Measuremen, Elecroncs, Compung and Communcaons, Vol. 50, o. (1-), pp , 009. [] T. Rabe, A. Shalaby, B. Abdulha and A. El-Rabbany, Moble Vson-based Vehcle Trackng and Traffc Conrol, Proceedngs of he IEEE Ffh Inernaonal Conference on Inellgen Transporaon Sysems, pp , 00. [3] Z. Sun, G. Bebs and R. Mller, On-Road Vehcle Deecon: A Revew, IEEE Transacons on Paern Analyss and Machne Inellgence, Vol. 8, o. 5, pp , 006. [4] X. L, K. Wang, W. Wang and Y. L, A Mulple Objec Trackng Mehod Usng Kalman Fler, Proceedngs of he IEEE Inernaonal Conference on Informaon and Auomaon, pp , 01. [5] M. abaee, A. Pooyafard and A. Olfa, Enhanced Objec Trackng wh Receved Sgnal Srengh usng Kalman Fler n Sensor eworks, Proceedngs of Inernaonal Symposum on Telecommuncaons, pp , 008. [6] Y. Fang and B. Da, An Improved Movng Targe Deecng and Trackng Based on Opcal Flow Technque and Kalman Fler, Proceedngs of he Fourh Inernaonal Conference on Compuer Scence and Educaon, pp , 009. [7] W.Y. Kow, W.L. Khong, Y.K Chn, I. Saad and K.T.K. Teo, CUSUM-Varance Rao Based Markov Chan Mone Carlo Algorhm n Overlapped Vehcle Trackng, Proceedngs of Inernaonal Conference on Compuer Applcaons and Indusral Elecroncs, pp , 011. [8] M.S. Arulampalam, S. Maskell,. Gordon and T. Clapp, A Tuoral on Parcle Fler for Onlne onlnear/on- Gaussan Bayesan Trackng, IEEE Transacon on Sgnal Processng, Vol. 50, o., pp , 00, [9] H.P. Lu, F.C. Sun, L.P. Yu and K.Z. He, Vehcle Trackng usng Sochasc Fuson-based Parcle Fler, Proceedngs of IEEE/RSJ Inernaonal Conference on Inellgen Robos and Sysems, pp , 007. [10] H. L, Y. Wu and H. Lu, Vsual Trackng Usng Parcle Flers wh Gaussan Process Regresson, Proceedngs of he 3 rd Pacfc Rm Symposum on Advances n Image and Vdeo Technology, pp , 009. [11] W.L. Khong, W.Y. Kow, L. Angelne, I. Saad and K.T.K. Teo, Overlapped Vehcle Trackng va Enhancemen of Parcle Fler wh Adapve Resamplng Algorhm, Inernaonal Journal of Smulaon, Sysems, Scence and Technology, Vol. 1, o. 3, pp , 011. [1] X. Fu and Y. Ja, An Improvemen on Resamplng Algorhm of Parcle Fler, IEEE Transacon on Sgnal Processng, Vol. 58, o.10, pp , 010, [13] K. ummaro, E. Koller-meer and L.V. Gool, Colour Feaures for Trackng on-rgd Objecs, Specal Issue on Vdeo Survellance Chnese Journal of Auomaon, Vol. 9, pp , 003. [14] M. Boumedene, A. Quamr and M. Keche, Vehcle Deecon Algorhm Based on Horzonal/Vercal Edges, 7h Inernaonal Workshop on Sysems, Sgnal Processng and Ther Applcaons, pp , 011. [15] H. Sheng, Q. We, C. L and Z. Xong, Robus Mulplevehcle Trackng va Adapve Inegraon of Mulple Vsual Feaures, Journal on Image and Vdeo Processng, Vol., pp. 1-19, 01. [16] W.L. Khong, W.Y. Kow, Y.K. Chn, I. Saad and K.T.K. Teo, Overlappng Vehcle Trackng va Adapve Parcle Fler wh Mulple Cues, Proceedngs of Inernaonal Conference on Conrol Sysem, Compung and Engneerng, pp , 011. [17] M.S. Khald, M.U. Ilyas, M.S. Sarfaraz and M.A. Ajaz, Bhaacharyya Cocen n Correlaon of Gray-Scale Objecs, Journal of Mulmeda, Vol. 1 o. 1, pp , 006. [18] S.C. Park, S.H. Lm, B.K. Sn, and S.W. Lee, Trackng non-rgd Objecs usng Probablsc Hausdorff Dsance Machng, Journal of Paern Recognon, Vol. 38, o. 1, pp ,