Effect of Resampling Steepness on Particle Filtering Performance in Visual Tracking

Transcription

1 102 The Inernaonal Arab Journal of Informaon Technology, Vol. 10, No. 1, January 2013 Effec of Resamplng Seepness on Parcle Flerng Performance n Vsual Trackng Zahdul Islam, Ch-Mn Oh, and Chl-Woo Lee School of Elecroncs and Compuer Engneerng, Chonnam Naonal Unversy, Souh Korea Absrac: Ths paper presens a profcenly developed resamplng algorhm for parcle flerng. In any flerng algorhm adopng he percepon of parcles, especally n vsual rackng, resamplng s an essenal process ha deermnes he algorhm s performance and accuracy n he mplemenaon sep. I s usually a lnear funcon of he wegh of he parcles, whch deermnes he number of parcles coped. If we use many parcles o preven sample mpovershmen, however, he sysem becomes compuaonally oo expensve. For beer real-me performance wh hgh accuracy, we nroduce a Seep Sequenal Imporance Resamplng (S-SIR algorhm ha can requre fewer hghly weghed parcles by nroducng a nonlnear funcon no he resamplng mehod. Usng our proposed algorhm, we have obaned very remarkable resuls for vsual rackng wh only a few parcles nsead of many. Dynamc parameer seng booss he seepness of resamplng and reduces compuaonal me whou degradng performance. Snce resamplng s no dependen on any parcular applcaon, he S-SIR analyss s approprae for any ype of parcle flerng algorhm ha adops a resamplng procedure. We show ha he S-SIR algorhm can mprove he performance of a complex vsual rackng algorhm usng only a few parcles compared wh a radonal SIR-based parcle fler. Keywords: Resamplng, parcle fler, mul-par colour hsogram, seepness parameer, objec rackng. Receved December 27, 2010; acceped March 1, Inroducon Parcle flerng s a Sequenal Mone Carlo (SMC mehod ha has demonsraed srong poenal for sgnal- and mage-processng applcaons. I performs hree opera-ons sequenally: generang new parcles samplng sep, compung parcle weghs mporance sep and resamplng [13]. More specfcally, a parcle fler s a combnaon of wo elemens: Sequenal Imporance Samplng (SIS [4, 13] and resamplng. Ths combnaon of SIS and resamplng s called Sequenal Imporance Resamplng (SIR. In he SIS algorhm, afer mulple eraons, only very few parcles have non-zero mporance weghs. Ths phenomenon s ofen descrbed as wegh degeneracy or sample mpovershmen. An nuve soluon s o mulply he parcles wh hgh normalzed mporance weghs and dscard hose wh low normalzed mporance weghs, whch can be done n he resamplng sep. In pracce, however, curren resamplng algorhms canno really preven wegh degeneracy; hey only reduce he calculaon me by dscardng parcles assocaed wh nsgnfcan weghs. In he proposed Seep Sequenal Imporance Resamplng (S-SIR algorhm, we change he convenonal resamplng prncple of SIR by usng a nonlnear funcon ha aenuaes parcles and uses fewer more effecve and hgher-weghed parcles. The seepness parameer n S-SIR can conrol he number of he bes parcles on he bass of wegh. Resamplng usually bu no necessarly occurs beween wo mporance samplng seps. I can be performed a every sep or only f s regarded as necessary. In our proposed S-SIR algorhm, he resamplng schedule has been chosen deermnscally nsead of dynamcally. In a deermnsc framework, resamplng s done a every k me seps (usually k=1. In a dynamc schedule, a sequence of hresholds (varyng me consan s esablshed and he varance of he mporance wegh s monored; resamplng s done only when he varance s over he hreshold. The srengh of he resamplng sep n he SIS algorhm has been verfed by many researchers, as descrbed n [11], bu snce also causes addonal varaon, addonal adjusmens are needed. Furhermore, he performance of a rackng sysem depends grealy on he arge objec represenaon and he smlary measuremen beween he arge and he reference objec, whch s called he measuremen model or he observaon model. Mos of he proposed rackng algorhms are applcaon dependen [2, 5, 17]. Many rely on a sngle cue, for example, color, whch can be chosen accordng o he applcaon conex. Color-based rackng has some advanages, bu here can be dsadvanages o havng an objec n a fla color. An effcen color-based arge represenaon can be made usng mulple regons of he color hsogram by mulple negral mages [14], whch s a Mul-Par Hsogram (MPH mehod; s very helpful for dealng wh occlusons. In hs paper,

2 Effec of Resamplng Seepness on Parcle Flerng Performance n Vsual Trackng 103 our S-SIR-based objec rackng mehod s drven by an MPH-based measuremen echnque. The mos heavly weghed parcles are locaed n he cenral regon of he arge by a weghng funcon, because he oher areas of he arge are no as mporan as he cener. The Bhaacharyya coeffcen [1] s used as a merc o calculae he smlary of he MPH. The res of paper s organzed as follows. Secon 2 descrbes work relaed o our curren sudy. Secon 3 presens a bref overvew of parcle flers and resamplng algorhms. Secon 4 nroduces he proposed S-SIR algorhm. The mplemenaon algorhm s dscussed n secon 5 wh proposed human body descrpor used for rackng wh an MPH. The resuls of expermens usng wo real-me vdeos wh severe occlusons are dscussed n secon 6; an evaluaon and comparson sudy are also presened. Concludng remarks are gven n secon Movaon and Relaed Work 2.1. Movaon A SIR-based parcle fler racks mulple hypoheses smulanously; each hypohess s represened by a sample, called a parcle, from a weghed se of hypohess samples (parcles. A me, hs se consss of n objec saes 1 n x,..., x and her assocaed weghs 1 n w,..., w. The parcle se s a dscrere approxmaon of he poseror dsrbuon of he real objec sae gven he observaons up o me : p(x y 0:. A he nex sep, he parcles are resampled accordng o her weghs. Ths s done o decrease he number of low-weghed parcles and ncrease he number of parcles wh hgh weghs. Anoher reason s ha he esmaed sae s he weghed average of all parcles; hence, hs funcon drecly affecs he esmaes. For SIR o be successful, a large number of samples parcles N s needed for wo reasons: 1. To oban a good approxmaon of p(x y 0:. 2. To be capable of recoverng from objec loss and o fnd mulple nsances f more han one objec s vsble. However, he sze N s drecly relaed o he compuaonal cos and should be kep as low as possble. Ths s he key fac movang us o use he dynamc seepness parameer n resamplng. We have seen how he seepness effec of resamplng mproves he rackng performance even wh a very small N parcles Relaed Sudes To enhance he effcency of parcle flerng for small N, many mprovemens have been suggesed. For example, herarchcal mehods [3] usng a coarse-ofne approach are used o fnd he real modes of objecs whou geng suck n local opma. Oher mehods nvolve sophscaed resamplng and/or predcon [12]. However, lle research has focused on mprovng he resamplng funcon for excellen performance wh few parcles. By usng our proposed seepness parameer n resamplng, we can dynamcally conrol he parcle number as desred. Several approaches have been used o mprove he resamplng sraegy n vsual rackng. Sysemac resamplng wh an adapve emplae for vsual rackng has been proposed [16]. Sysemac resamplng had already been esablshed n [13], and he newer mehod s sll a lnear-ype funcon. A samplng sraegy amed a reducng compuaonal complexy n he parcle flerng framework has also been proposed [15]. Ths sraegy combnes parcle flerng wh a ranson pror and an unscened Kalman fler. Our approach dffers n ha s a nonlnear ype; s deally sued o real-me, hghly accurae vsual rackng. In hs arcle, we ncorporae a nonlnear funcon no hs resamplng algorhm so chooses only a few of he bes parcles wh hgh wegh by reducng he search area. All hgh-wegh parcles are concenraed appropraely on he racked objec, reducng he possbly of rackng falure and enhancng performance sgnfcanly. 3. Parcle Fler Overvew In parcle flerng, we wan o compue he flered esmaes of x ha s, p(x y, based on he se of all avalable measuremens up o me. In Bayesan esmaon, p(x y s compued recursvely, ha s, n erms of he poseror densy a he prevous me sep, p(x -1 y -1. A parcle fler algorhm uses a se of weghed samples drawn from he poseror dsrbuon o approxmae negrals as dscree x,w sums gven a se of 1: -1 1: -1 N random samples =1,2,...,N w 1: -1 are he respecve weghs and y1:-1 are he avalable measuremens up o me. Accordng o he SIS sraegy, he poseror dsrbuon can be compued as: N 1: δ =1 p( x y w ( x x, where where δ (. s he Drac dela funcon. I s usually mpossble o sample from he poseror dsrbuon drecly. Ths maer can be resolved by drawng samples from a proposal dsrbuon q ( x1: 1 y1: 1. Choosng he proper proposal dsrbuon s an mporan sep when usng an mporance samplng algorhm. The mos popular choce of proposal dsrbuon s he pror dsrbuon because of s ease of calculaon. The proposal dsrbuon can be expressed as: p( x x =q( x x,y 1 1 =1,2,...,N (1 (2

3 104 The Inernaonal Arab Journal of Informaon Technology, Vol. 10, No. 1, January 2013 If he pror dsrbuon s seleced as he proposal dsrbuon, he mporance wegh calculaon can be expressed smply as: w = w 1 p( y q( x x p( x 1: 1 x, y x 1: 1 The mean sae of an objec s esmaed a each me sep by: N E[x ] = w x ˆ =1 (4 4. Nonlnear Resamplng Algorhm In resamplng, parcles wh large weghs are replcaed, and hose wh neglgble weghs are removed. Resamplng maps he weghed random measure ~ x, w ( x } ono he equally weghed { 0: k 0: random measure { x,n } 0: by samplng unformly wh replacemen from sample space on he bass of he probables. Many mproved parcle flers focus on resamplng, for nsance, ha n [10], n whch he auhors proposed usng a parally deermnsc reallocaon scheme nsead of resamplng o overcome he exra varaon arsng n resamplng. We modfy he SIR-based parcle fler by changng he resamplng funcon o a nonlnear funcon. In real-me vsual rackng, he SIR fler works well, bu effecve sorng of hgher-wegh parcles n every eraon s compuaonally expensve, and rackng falure becomes more lkely. Our ulmae goal can be dvded no wo pars. Frs, we wan o use fewer of he bes-weghed parcles; second, by reducng he number of parcles, we wan o ge he bes rackng oupu. Our proposed mehod reduces calculaon me and uses he lowes number of he hghes-wegh parcles by usng a seepness funcon. The seepness parameer can also conrol he number of he bes parcles used, whch s manly applcaon dependen, as desred. The radonal resamplng algorhm s a lnear mappng funcon ha copes or replaces parcles wh hgh wegh. I can be expressed as: Γ = w.n Where Γ s he new sored parcles se, w s he relevan wegh, and n s he parcle number. We can copy he more effecve parcles by dscardng hose assocaed wh nsgnfcan weghs usng: Ξ = a* ( exp( b* (w + c where Ξ s new assgned wegh wh non-lnear mappng, b s an aenuang facor (seep parameer, and a and c are arbrary consans (a, b 0. The number of parcles coped for resamplng can be conrolled by he seepness parameer b, as shown n Fgure 1. Besdes, how we can assgn suable value of (3 (5 (6 b s dscussed n he expermen secon. Fgure 1 shows ha hs nonlnear mappng helps aenuae parcles by dscardng low-wegh parcles, whch s beer han he lnear mappng used n convenonal resamplng. To normalze equaon 6, we can wre as: W = Ξ N =1 w (7 Fnally, equaon 5 can be rewren wh he help of equaon 7 as: Γ = round ( W. n (8 However, hs sraghforward algorhm creaes he problem of wegh degeneraon. Resamplng algorhms have been appled o overcome hs problem. Fgure 1. Effec of seepness parameer n resamplng based on wegh. To clarfy our proposed resamplng, we can compare o he radonal resamplng sraegy shown n Fgure 2, whch s dscussed n many arcles, e.g., n [6, 9, 10, 13, 15, 16]. As we see from Fgure 1, he seepness parameer conrols he resampled parcles nonlnearly, as llusraed n Fgure 3. We can boos he seepness by ncreasng b, and he ncreased seepness can accumulae he bes weghed parcles n a more concenraed way. However, ncreasng b oo much may creae anoher problem by losng he basc mulmodaly of he parcle fler. Thus, we mus be careful o choose he bes seepness parameer b, whch may be applcaon dependen. Fgure 2. Basc resamplng sraegy.

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5 Effec of Resamplng Seepness on Parcle Flerng Performance n Vsual Trackng 105 dynamc s gven as X +1 =Ax +Bx -1. Marces A and B could be deermned from a se of sequences n whch he correc racks have been obaned. Fgure 3. S-SIR based resamplng sraegy nonlnear. Ths example uses a 10-parcle smulaon. As we ncrease b, he bes weghed parcle s coped many mes by dscardng oher, lower-weghed parcles. 5. Parcle Flerng Based Implemenaon Parcle flerng esmaes he proposal dsrbuon usng samples from prevous poseror dsrbuons. Ths esmaon requres approxmaon, whch s weghed by he observaon model. Robus rackng demands a robus observaon model Measuremen Objec Feaure Descrpor In hs paper, he racked human body s consdered o conss of varous recangle regons. We nroduce he MPH, whch uses an negral-mage-based represenaon [14] ha characerzes he human body usng dealed spaal nformaon. The deals of MPH can be found from our prevous paper n [8, 9] Colour Measuremen Model To acheve robusness agans non-rgdy, roaon, and paral occluson, we focus on he color dsrbuons as arge models. The deals of hs proposed color model also can be found from our prevous paper n [8, 9]. We use a mulple-regon color hsogram as one of he observaon measuremens o wegh he sample se. The observaon accuracy depends on he objec's feaures. We adop a Gaussan densy for he lkelhood funcon of he measured color hsogram The Moon Model We model he sae locaon n each frame of a vdeo. The sae space s represened n he spaal doman as X=(x, y. The sae space for he frs frame s nalzed manually by selecng he objec of neres n a vdeo scene usng a recangle. A second-order auoregressve dynamc s chosen from he parameers used o represen our sae space,.e., (x, y. The 5.3. The Observaon and Lkelhood Model The observaon model we have found usng he measuremen model n subsecon 5.1, whch s used o measure he observaon lkelhood of he samples. Ths s mporan n objec rackng. The fler correcs he predced esmaon by usng he observed daa. The overall lkelhood calculaon based on he MPH s gven by: L ( y x D ( p, q = LMPH ( y MPH, x where D = ds[ p, q ] s he dsance beween he reference hsogram p of he objecs o be racked and he hsogram q compued from mage n he regon defned by he sae vecor x and y denoes he measuremen vecor, whch s composed of he measuremen vecors y MPH, from he MPH-based colour cue. 6. Expermens and Resuls (9 We verfy he performance of our algorhm expermenally usng wo dfferen vdeo sequences from our own daabase and anoher well-known daabase, CAVIAR [7]; we am o rack a pre-seleced movng person. In he frs sequence, crcleocc (500 frames, wo people are walkng oward each oher from oppose sdes of he frame. They mee, shake hands, and crcle each oher; our subjec s compleely occluded more han hree mes. The second sequence, OneShopOneWa2cor (211 frames, was obaned from he CAVIAR [7] daabase. Anoher person causes a lenghy occluson of he arge. Fgure 4 compares he rackng performance of our proposed resample-based algorhm wh ha of he radonal algorhm usng 100 and 10 parcles for crcleocc. For hs comparson, we use frames 71, 99, 109, 181, 296, and 480 n all he ess. Our proposed sysem works well even wh only 10 parcles, as shown n Fgure 4-c. The possbly of rackng falure when usng only 10 parcles was drascally reduced. The overall performance can be verfed by he horzonal and vercal red bars shown n each frame, whch represen he probably denses of he esmaed sae. In Fgure 4-b, we can see ha he probably denses become scaered o fnd he bes weghed parcle for he nex sae esmaon. Ths works well somemes bu only when usng a large number of parcles. However he possbly of rackng falure remans owng o he many real-me challenges n vsual rackng. In hs vdeo, we use seepness parameer b=1000.

6 106 The Inernaonal Arab Journal of Informaon Technology, Vol. 10, No. 1, January 2013 a Proposed new resamplng: 100 parcles. b Tradonal resamplng: 100 parcles. c Proposed new resamplng: 10 parcles. d Tradonal resamplng: 10 parcles. Fgure 4. Trackng n crcleocc wh radonal and proposed resamplng usng 100 and 10 parcles. Overall performance can be verfed by he horzonal and vercal red bars shown n each frame, whch represen he probably denses of he esmaed sae. a Proposed new re-samplng: 100 parcles. b Tradonal SIR based re-samplng: 100 parcles. Fgure5-c. In boh cases, we use seepness parameer b=500. A more quanave resul s dscussed n he followng secons The Error Merc In he prevous secon, our proposed sysem was evaluaed qualavely. The Roo Mean Squared Error ( mehod n he sae space has also been used o evaluae he performance of our algorhm. The can be formulaed by: ( = 0.5(( g gˆ 2 + ( h hˆ where ( gˆ, ˆ h sands for he upper-lef corner coordnaes of he rackng box deermned by he cenral poson correspondng o he sae esmaed by he parcle fler n he frame. The ground ruh saes (g, h correspond o he rue posons of he objec and have been generaed by manually creang a rackng box surroundng he objec n he es vdeos The Performance Evaluaon 2 (10 We evaluae our proposed sysem wh 100 and 10 parcles usng dfferen seepness parameers o observe he rackng oupu of he wo example vdeos. The graph for he crcleocc vdeo sream wh dfferen seepness parameers s shown n Fgures 6 and 7. When b=1 and 100, he rackng performance s no as good as desred, and s beer when b=500 o 5000, remanng almos consan n ha range. A more numercal analyss s gven n Table 1. c Proposed new re-samplng: 10 parcles. d Tradonal SIR based re-samplng: 10 parcles. Fgure 5. Trackng n OneShopOneWa2cor wh radonal and proposed resamplng usng 100 and 10 parcles. Overall performance can be verfed by he horzonal and vercal red bars shown n each frame, whch represen he probably denses of he esmaed sae. Fgure 5 compares he performance of our proposed resamplng mehod and a radonal SIR-based resamplng algorhm wh 100 and 10 parcles for OneShopOneWa2cor. In hs expermen, we use frames 140, 261, 288, 301, and 334 n all ess. As wh he performance llusraed n Fgure 4, hs OneShopOneWa2cor vdeo sequence also verfes he effecveness of our proposed sysem. Usng 10 parcles, he radonal SIR-based resamplng fals o rack he arge, as shown n Fgure 5-d, whereas our proposed sysem works very well, as shown n Fgure 6. Performance of proposed resamplng for crcleocc vdeo sream wh dfferen seepness parameers (b=1 o 5000 usng 100 parcles. Fgure 7. Performance of proposed S-SIR mehod and convenonal SIR fler.

7 Effec of Resamplng Seepness on Parcle Flerng Performance n Vsual Trackng 107 Table 1. Performance of proposed resamplng and SIR fler for crcleocc vdeo sream wh dfferen seepness parameers usng 100 parcles. Seep Par.b Max Mn Avg Trad. SIR P N/A We also compared our resuls wh hose of a convenonal SIR fler. Our proposed S-SIR performed much beer han he SIR. For example, when b=1000, he maxmum and average error are and 7.2, respecvely. In conras, for he convenonal SIR, he maxmum and he average error are and 8.15, respecvely. Also, he las row of hs Table shows he number of bes parcles used (P a dfferen seepness parameers b. As we ncrease b, he number of parcles used decreases. Ths reduces he calculaon me, makng our sysem faser. However, f we ncrease he seepness parameer oo much, he parcle fler may lose s mul-modaly, whch creaes anoher problem. We chose he opmum seepness on he bass of he rackng envronmen and he desred resul. The graph n Fgure 7 compares he performance of he proposed S-SIR mehod and a convenonal SIR fler. Our proposed algorhm works well wh only 10 parcles, whereas he SIR-based parcle fler oally fals o rack he objec. The graph n Fgure 8 shows he rackng performance wh dfferen seepness facors wh only 10 parcles. Tha n Fgure 9 compares he performance of he proposed S-SIR and a SIR-based parcle fler wh 10 and 100 parcles. Table 2 summarzes he a dfferen seepness parameers wh only 10 parcles. The las column of hs Table also shows he correspondng rackng performance of a convenonal SIR-based parcle fler. Fgure 8. Performance of proposed resamplng for crcleocc vdeo sream wh dfferen seepness parameers b(b= 1 o 1000 usng 10 parcles. Fgure 9. Performance of proposed S-SIR and SIR wh 100 and 10 parcles. Table 2. Performance of proposed resamplng and a SIR fler for crcleocc vdeo sream wh dfferen seepness parameers usng 10 parcles. Seep Par. b Max Mn Avg Trad. SIR P N/A Table 3. Performance of proposed resamplng and SIR fler for OneShopOneWa2cor vdeo sream wh dfferen seepness parameers usng 100 parcles. Seep Par. b Max Mn Avg Trad. SIR P N/A The graph for he OneShopOneWa2cor vdeo sream a dfferen seepness parameers s shown n Fgures 10 and 11. Fgure 10 shows he parformance usng 100 parcles wh dfferen seepness parameers, and Fgure 11 shows he performance usng only 10 parcles. In boh cases, he bes-uned seepness parameer s b=500. Tables 3 and 4 summarze he a dfferen seepness parameers wh 100 and 10 parcles, respecvely. The las column of hese Tables also shows he correspondng rackng performance of a convenonal SIR-based parcle fler. For example, when b=500, he maxmum and average error are and 2.87, respecvely. In conras, wh a convenonal SIR, he maxmum and average error are and 4.3, respecvely. As noed above, Table 4 llusraes ha, wh 10 parcles, he SIR-based fler oally fals o rack he objec. For he SIR-based fler wh 10 parcles, he maxmum and average error are and 96.9, respecvely whereas wh our

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9 108 The Inernaonal Arab Journal of Informaon Technology, Vol. 10, No. 1, January 2013 proposed sysem, hey are 11.4 and 3.92, respecvely (seepness parameer b=500. Noe ha we mus carefully une he seepness parameer, as we do no wan o lose he mul-modaly of he parcle fler. Ths may happen f we ncrease he seepness parameer oo much (more han 10,000. Fgure 10. Performance of proposed resamplng for OneShopOneWa2cor vdeo sream wh dfferen seepness parameers b usng 100 parcles (b=1 o Fgure 11. Performance of proposed resamplng for OneShopOneWa2cor vdeo sream wh dfferen seepness parameers b usng 10 parcles (b=1 o Table 4. Performance of proposed resamplng and SIR fler for OneShopOneWa2cor vdeo sream wh dfferen seepness parameers usng 10 parcles. Seep Par. b Max Mn Avg Trad. SIR P N/A 7. Conclusons A new parcle fler wh S-SIR-based resamplng has been proposed n hs paper. The proposed resamplng desgn addresses problems wh effcen conrol of he bes weghed parcles by aenuaon. The seepness facor can conrol he number of hgh-wegh parcles as desred. I also reduces he calculaon me durng rackng, and even a few parcles can provde sasfacory rackng. Ths S-SIR algorhm mproves he objec-rackng performance compared o a convenonal SIR-based fler. The proposed nonlnear ype of resamplng can deermne he mos mporan parcles and aenuae he oher parcles very effcenly. Also, from he expermenal resuls, we can conclude ha hs proposed algorhm mnmzes he degradaon of real-me performance and remarkably reduces he compuaonal complexy. Wh our opmally uned seepness parameer, we can oban our desred rackng performance wh few parcles, whereas a convenonal sysem canno rack a all wh a small number of parcles. Acknowledgemens Ths research was suppored by The Mnsry of Knowledge Economy (MKE, Korea, parally under he Informaon Technology Research Cener (ITRC suppor program and parally under he human resources developmen program for Convergence Robo Specalss suppor program supervsed by he Naonal IT Indusry Promoon Agency (NIPA (NIPA-2010-C and (NIPA C References [1] Aherne F., Thacker N., and Rocke P., The Bhaacharyya Merc as an Absolue Smlary Measure for Frequency Coded Daa, Kyberneka, vol. 34, no. 4, pp , [2] Aruar L., Lyudmla M., and Davd B., Srucural Smlary-Based Objec Trackng n Mulmodaly Survellance Vdeos, Machne Vson and Applcaons, vol. 20, no. 2, pp , [3] Deuscher J., Blake A., and Red I., Arculaed Body Moon Capure by Annealed Parcle Flerng, n Proceedngs of Compuer Vson and Paern Recognon, USA, vol. 2, pp , [4] Douc R. and Cappe O., Comparson of Resamplng Schemes for Parcle Flerng, n Proceedngs of he 4 h Inernaonal Symposum on n Image and Sgnal Processng and Analyss, pp , [5] Ganesan K. and Jalla S., Vdeo Objec Exracon Based on a Comparave Sudy of Effcen Edge Deecon Technques, The Inernaonal Arab Journal of Informaon Technology, vol. 6, no. 2, pp , [6] Green P., Reversble Jump Markov Chan Mone Carlo Compuaon and Bayesan Model Deermnaon, Bomerka, vol. 82, no. 4, pp , [7] Homepages, avalable a: hp://homepages.nf. ed.ac.uk/rbf/caviardata1, las vsed [8] Islam M., Oh C., and Lee C., An Effcen Mulple Cues Synhess for Human Trackng Usng A Parcle Flerng Framework, Inernaonal Journal of Innovave Compung, Informaon and Conrol, vol. 7, no. 6, pp

10 Effec of Resamplng Seepness on Parcle Flerng Performance n Vsual Trackng , [9] Islam M., Oh C., and Lee C., Modfed Re- Samplng Based Parcle Fler for Vsual Trackng wh MPH, n Proceedngs of 10 h IEEE Inernaonal Conference on Compuer and Informaon Technology, Bradford, pp , [10] Lu J., Chen R., and Logvnenko T., A Theorecal Framework for Sequenal Imporance Samplng wh Resamplng, Sequenal Mone Carlo Mehods n Pracce, Eds., Sprnger Verlag, Berln, [11] Lu J. and Chen R., Blnd Deconvoluon Va Sequenal Impuaon, Journal of Amercan Sascal Assocaon, vol. 90, no. 430, pp , [12] P M. and Shephard N., Flerng Va Smulaon: Auxlary Parcle Flers, Journal of he Amercan Sascal Assocaon, vol. 94, no. 446, pp , [13] Sanjeev A., Smon M., Nel G., and Tm C., A Tuoral on Parcle Flers for Onlne nonlnear/non-gaussan Bayesan Trackng, IEEE Transacons on Sgnal Processng, vol. 50, no. 2, pp , [14] Vola P. and Jones M., Rapd Objec Deecon Usng A Boosed Cascade of Smple Feaures, n Proceedngs of he IEEE Conference on Compuer Vson and Paern Recognon, vol. 1, pp. I-511-I-518, [15] Wang F. and Ln Y., Improvng Parcle Fler wh A New Samplng Sraegy, n Proceedngs of 4 h Inernaonal Conference on Compuer Scence and Educaon, Nannng, pp , [16] Wu G. and Tang Z., A New Resamplng Sraegy abou Parcle Fler Algorhm Appled n Mone Carlo Framework, n Proceedngs of Second Inernaonal Conference on Inellgen Compuaon Technology and Auomaon, Hunan, pp , [17] Yunqang C. and Yong R., Real Tme Objec Trackng n Vdeo Sequences, Sgnals and Communcaons Technologes, Ineracve Vdeo, Sprnger Berln Hedelberg, Par 2, pp , engneerng n nellgen mage meda and nerface lab, Chonnam Naonal Unversy, Souh Korea. Hs oher curren research neress nclude compuer vson, 3D objec, human and moon rackng and rackng arculaed body, genec algorhm ec. Ch-Mn Oh receved hs BSc and MSc degree n compuer engneerng from Chonnam Naonal Unversy, Gwang-ju, Korea n 2007 and 2009 respecvely. Snce February 2009, he has been pursung he PhD degree n School of Elecroncs and Engneerng, Chonnam Naonal Unversy. Hs research neress nclude gesure recognon and arculaed body rackng. Chl-Woo Lee receved hs BSc and MSc degrees n elecronc engneerng from Chung-Ang Unversy n 1986 and 1988 respecvely n Seoul, Korea. And he receved PhD also n elecronc engneerng n 1992 from Unversy of Tokyo, Japan. Snce 1996, he has been a professor, Depmen of Compuer Engneerng, Chonnam Naonal Unversy n Korea. He has worked as senor researcher a laboraores of mage nformaon scence and echnology for four years, form 1992 o 1996, and a ha me he had an exra pos of vsng researcher a Osaka Unversy n Osaka, Japan. From January, 2001, he has vsed Norh Carolna A and T Unversy as a vsng researcher and jonly worked on several dgal sgnal processng projecs. He s now a drecor of wo research nsues; moble devce research cenre and culure echnology research nsue, and hose are fnancally suppored by he governmen of Rep. Korea. Up o now, hs research work has been assocaed wh mage recognon and mage synhess. Hs research neress nclude compuer vson, compuer graphcs, and vsual human nerface sysem. And he s also very neresed n realzaon of realme sensor sysem ha can be aware of conex of crcumference. Zahdul Islam receved hs BSc and MSc degrees from he Deparmen of Appled Physcs and Elecronc Engneerng, Unversy of Rajshah, Bangladesh, n 2000 and 2002 respecvely. In 2003, he joned as a lecurer n he Deparmen of Informaon Communcaon Engneerng, Islamc Unversy, Bangladesh. He s currenly, workng on he developmen of vsual objec rackng sysem wh varous aspecs as a PhD canddae of compuer