Fragmets based tracig with adaptive multi-cue itegratio Abstract Lichua Gu *, Jiaxiao Liu, Chegi Wag School of Computer ad Iformatio, Ahui Agriculture Uiversity, Hefei Ahui, No.130 West Chagiag Road Chia, 230036 Received 1 Jauary 2014, www.tsi.lv I this paper, we address the issue of part-based tracig by proposig a ew fragmets-based tracer with multi-cue itegratio. First the target template is divided ito multiple fragmets to get the cotributio of each fragmet to the possible positios of the target i the curret frame, similarity measure is used i edge histogram ad HSV histogram i every fragmet, ad the weights of cues itegratio are computed adaptively. Secod we preset a fragmet template update mechaism with the discrimiatio betwee occlusios ad appearace chages. The template is uchaged whe the target is occluded ad some fragmets of template are updated i the case of appearace chages. I the experimets we use the idoor ad outdoor test videos which cotai the illumiatio chages, occlusios, ad the appearace chages of targets. The experimetal results show that our approach has strog robustess ad high tracig accuracy. Keywords: Target tracig, Multi-cue itegratio, Fragmets tracig, Template update 1 Itroductio The obect tracig i a accurate way is essetial for applicatios lie activity aalysis, ma-machie iteractio ad visual surveillace. However, i the actual process of tracig, target appearace chages (as show i Figure 1 (a)) ad occlusios (as show i Figure 1 (b)) will affect the stability of the tracig algorithm, which leads to iaccurate target tracig ad eve target lost. As a result, how to effectively deal with target appearace chages ad occlusios has always bee oe of the difficulties i target tracig problems. A commo solutio to the above problems is tracig target by the itegratio of multiple visual cues, amely each cue provides a lielihood value for possible positios of the target i the ext frame, ad these cues are itegrated accordig to the lielihood values to locate the fial output (Figure 2). I recet te years, a few of tracig algorithms usig multi-cue itegratio have bee proposed, say, Birchfield proposed usig itesity gradiet ad color histograms to trac people head [1]. The disadvatage of this method is that durig tracig the cues do ot always provide reliable iformatio about the target obect, sice the equal weights are assiged to each cue ad the cues weights are fixed i the whole video sequece. I order to solve this problem, Triesch ad vo der Malsburg proposed a ew itegratio framewor [2], which ca tae advatage of the ucertaity of each cue to adaptively adust each cue cotributio to the tracig results. Followig this framewor appeared may adaptive cues itegratio algorithms, as show i [3-8]. These algorithms itegrated a variety of cues, complemeted each other ad improved the accuracy of the tracig process to some extet. (a) (b) FIGURE 1 Difficulties i tracig coditios: (a) target s variable appearace; (b) occlusios Aother solutio to the above problems is to trac movig targets based o fragmets. I the tracig of * Correspodig author - E-mail: glc@ahau.edu.c 126
(a) (b) (c) FIGURE 2 Cues based o differet features: (a) Origial image; (b) HSV histogram; (c) Edge histogram huma, for example, huma is divided ito the head, torso ad limbs [9-11]. This approach geerally required target model is ow or a priori premise ad is ot suitable for geeric target tracig. I order to solve this problem, some scholars put forward the geeral fragmet tracig algorithms [12, 13]. This id of method divided the target ito differet parts, but the divisio was arbitrary without cosiderig ay referece target model. I the process of tracig, this method weighted each fragmet, ad the cotributios of differet fragmets are combied through statistical method to get the fial output locatio of target. If there were target appearace chages or occlusios, the weight of correspodig fragmet would be small ad the impact o the overall goal would be small, thus the resolutio of target would be improved. The target tracig method based o template matchig has attracted icreasig attetio [14-17]. Traditioal image template matchig based tracig method is still widely used because of its simple computatios. This id of method extracted some cues as a template which will be ept uchaged, ad the looed for a regio whose cues were most similar to the template i curret frame. However, the target may be occluded durig motio, ad may also chage its appearace due to ow motio. I these cases the template eeds to be updated olie to trac the target accurately. The existig mai template update algorithms update template oce the chages of grey are detected i target regios. These algorithms are differet from oe aother oly i that the update rate or updated compoets are ot the same, without discrimiatio of appearace chages ad occlusios. The compariso of aforemetioed algorithms is give i Table 1. This paper's cotributios lie i: 1) our algorithm employs a adaptive cue itegratio scheme i each fragmet of the target. Similarity measure i edge histogram ad HSV histogram are used, the vote of each fragmet cotributes to the oit tracig result accordig to adaptive itegratio, ad the oes havig small values have little effect o the outcome. This allows us to achieve a better tracig accuracy i hadlig partial occlusios ad appearace chages. 2) Our algorithm ca update the fragmet template olie durig the tracig process. I the small fragmets the method detects the possible fragmets where appearace chages or occlusios occur, ad the taes correspodig update strategies accordig to the matchig of the small fragmets. The fragmet template overcomes the shortcomig of the traditioal template, which modelled the whole target without the spatial iformatio. The rest of this paper is orgaized as follows. The subsequet sectio describes the fragmet tracig algorithm. The a adaptive multi-cue itegratio model is give i Sectio 3. Sectio 4 presets a ew tracig algorithm of multi-cue itegratio based o fragmet, which is the mai cotributio of this paper. Sectio 5 is devoted to algorithm aalysis, icludig experimetal results o videos with differet tracig coditios, ad gives qualitative ad quatitative aalysis. The fial sectio cocludes this paper ad suggests the future directio of our research. 2 Fragmet tracig algorithm Fragmet tracig is a id of target tracig algorithm based o cues proposed by Adam [12]. The basic idea is dividig target widow ito multiple sub-fragmets ad represets the target accordig to the oit sub-fragmet histograms. This id of algorithm has good atiiterferece ad ati-occlusio, ad the computatio has othig to do with traced target size. Detailed descriptio is as follows: The divisio patter is show i Figure 3. The target template is: ( p ( ), p p p1,, T h b, p 127
p is the idex of fragmet, the target is divided ito fragmets, h (b) ad p are a histogram ad weight p respectively, where b=1, B, ad fragmet weight equals 1 for every fragmet. the histogram matchig distace betwee fragmet q, ad fragmet : x y, which is defied as d( p ) :( x, ( h ( b) h ( b)) p 2 B p q:( x, y ) b1 hp ( b) h ( ) q b :(, x, y ). (2) FIGURE 3 Vertical ad horizotal fragmet template Fragmet tracig is to fid the most similar regio to the template i curret frame. The tracig priciple is show i Figure 4. Assume that a previous estimate of the positio has bee got, ad each poit i the eighbourhood of this estimate is searched, the every coordiate poit i the searchig widow is a cadidate for the target i curret frame. The smaller d( p :( x, ) meas more similarity betwee the histograms. After ( xy, ) has traversed all the cadidate poits, we obtai the similarity betwee each cadidate target ad the curret template. The the curret target locatio ( xy ˆ, ˆ) is defied as: ( xˆ, yˆ ) arg mi( S( x, ), (3) ( xy, ) where is the set of all the cadidate locatios. To distiguish easily betwee occlusios ad appearace chages, after fidig the target locatio i curret frame accordig to the matchig distace betwee fragmet q: xˆ, yˆ i the target regio ad the correspodig fragmet p, we update the weight of p as p ( d( p ) exp( ), (4) :( xˆ, yˆ ) 2 d where is the variace of d( p d :( xˆ, yˆ )). For coveiece q: xˆ, yˆ is represeted as q i the sequel. FIGURE 4 Fragmet tracig priciple Let p =(dx,dy,h,w) be a rectagular patch i the template, whose cetre is displaced (dx,d from the template cetre, ad whose half width ad height are w ad h respectively. Let (x, be a hypothesized positio of the obect i the curret frame. The the patch p defies a correspodig rectagular patch i the image q ; x, y whose cetre is at (x+dx,y+d ad whose half width ad height are w ad h, respectively. Calculatig the histogram matchig distaces of each q : x, y i the cadidate regio ad the correspodig fragmet i the template, we ca get Formula (1) usig the liear weightig of all the matchig distaces to represet the similarity betwee cadidates ad target template. S( x, d( p, (1) 1 : ( x, ) where S( x, y ) is the similarity betwee the cadidate at the locatio ( xy, ) ad the template, ad d ( p :( x, ) is p 3 Adaptive multi-cue itegratio I complicated scee, it is difficult to obtai good tracig performace by sigle visual cue. To further improve tracig robustess, we eed itegrate other cues. Suppose F is a visual cue which has bee selected ad the fused cue is f fusio, the f fusio ca be represeted as: f where fusio 1 1 1 F is the weight of, (5) F. It is clear that differet cues have differet cotributios to the ability of idetifyig the whole target due to the impact of the eviromet, so differet cues should have differet weights. Sice there may be target s appearace chages or occlusios i real tracig, the weight of each cue i 128
the each frame of video sequece should be updated dyamically. There are two problems to be solved: oe is how to evaluate the weights accordig to differet cotributio of each cue i differet tracig eviromet, the other is how to update the weights i a steady ad real-time way. 4 Proposed tracig algorithm 4.1 TRACKING PROCESS This paper proposes a ew method of fragmets based tracig with multi-cue itegratio ad template updatig. The algorithm describes the target obect by a template, ad carries out tracig by searchig for the image regio i each frame of the tracig sequece with multiple cues similar to the template. This estimatio process is doe by splittig the target obect ito multiple arbitrary patches with each of them describig a differet part of the target ad accordigly providig the multi-cue iformatio. The template is adaptively updated i accordace with whether occlusios or appearace chages occur to alleviate the template drift. The tracig schedule is show i Figure 5. Step3: The idividual votes of the template patches are combied to obtai the oit tracig result. The weights of the template fragmets are computed accordig to the formula (4), if p 0.5, the histograms of q chaged a lot compared to correspodig p, that meas there might be occlusios or appearace chages. We call such q as ivalid fragmet. The oit similarity S( x, is defied i formula (6), ad the we ca get the optimum positio of the target i curret frame usig formula (3): d( p ), 0.5, :( x, p p S( x, (6) 1 0, 0.5. p Step4: We udge each ivalid fragmet to see if it is caused by occlusio or appearace chage. If there is occlusio, the go to step 2 directly without template update ad start the ext frame tracig; otherwise update the template ad the go to step 2. The detail is described i sectio 4.4. 4.2 CUES HISTOGRAM COMPUTATION FIGURE 5 The flow chart of our algorithm The steps of our algorithm are as follows. Step1: Target template is divided ito multiple fragmets usig the patch layouts give i Figure 2, ad the each of these fragmets is represeted with histograms of differet cues (colour, edge), ad Itegral Histogram data structure [18] is used to estimate them i a fast way. Step2: The search widow size is set to be (2M 1) (2M 1) i curret frame, amely the search radius is set to be M pixels from the previous target positio (M =7 i this paper, ad the search widow is 15 15 ). Matchig each template patch ad the correspodig image patch is the carried out by comparig their histograms usig the formula (13), ad the we obtai the values { d( p ) } by adaptive :( x, y ) weight itegratio. Each d( p :( x, ) idicates every fragmet votes o the possible positios of the target. The detail is give i sectio 4.3. I complicated scee, it is difficult to obtai good tracig performace by sigle visual cue. To improve tracig robustess, we eed itegrate other cues. It is very importat to select the strog discrimiate cues. This paper uses the itegratio of colour ad edge cues to costruct lielihood fuctio for the purpose of scatterig the impacts of all ids of chages. Colour is a maor cue to describe the target. There have bee may studies about the descriptio of target colour cues before. Ref. [19] proposed a colour histogram to describe the target colour cues, which has simple calculatio ad fast processig, ad is relatively robust i solvig problems such as partial occlusio, rotatio, etc. HSV histogram is employed to extract colour cue i each fragmet i this paper. I our experimets, the Hue, Saturatio ad Value are quatified to be 16, 4, ad 4. The three colour compoets are combied ito oe dimesioal cue accordig to the quatitative level: C 16H 4S V, (7) where C [0,1,,255], so we ca get 256 bis HSV histogram. Edge is aother commoly used cue to describe the target. This paper chooses gradiet directio histogram to describe the edge cue of the target [19]. The calculatio formulas are as follows: Horizotal gradiet G ( x, f ( x 1, f ( x 1,, (8) x 129
Vertical gradiet q:( x, y ) d( p ) 1 :( x, y ) d( p ) :( x, y ), (14) G ( x, f ( x, y 1) f ( x, y 1), (9) y Gradiet magitude m x y G x y G x y, (10) 2 2 (, ) x(, ) y(, ) Gradiet directio ( x, arcta( G ( x, / G ( x, ), (11) y where ( xy, ) [ / 2, / 2]. (x, should be mapped ito 0 ~ 2. ( x, Gx 0 ( x, 2 ( x, Gx 0 ad Gy 0 ( x, Gx 0 ad Gy 0 x. (12) [0,2 ) is divided ito bis with the width of each bi beig 2 / (=9 i the paper). Each bi value is got by coutig the pixels umber i it ad we ca obtai the edge histogram. 4.3 ADAPTIVE WEIGHT INTEGRATION I order to adapt to the eviromet ad the target appearace chages, the weight of each feature i each frame must be updated i real time. Ref. [21] proposed Kolmogorov-Smirov similarity measure criteria, K-S distace for short, to calculate the degree of similarity betwee the cadidate obect ad target template. Its value represets the differece of two histograms; the smaller K-S value meas smaller differece ad higher similarity. Based o K-S distace, the matchig distace for oe cue is defied as follows: d( p ) :( x, y ) B p q :( x, y ) b1 :( x, y ) ( h ( b) h ( b)) h ( b) h ( b) p q:( x, y ) 2. (13) d( p ) is the histogram matchig distace for cue betwee fragmet q, p ad fragmet x y :. We ca chage the weight of correspodig cue by formula (14), so as to automatically chage the ifluece of differet cues to global fuctios. A adaptive weight itegratio formula is defied as i (15): :( x, q :( x, y ) :( x, y ) 1 d( p ) d( p ). (15) It ca be see that automatically chagig the differet cues weights ca better reflect the cotributios of cues to the result. I experimets =1, 2, colour is the 1 1 first cue, edge is the secod, d( p ) ad 2 2 (, ) :( x, y ) :( x, y ) d p q are the histograms matchig distaces for colour ad edge cues betwee fragmet p ad fragmet q : x, y respectively. Whe the obect begis to eter the occlusio or chages appearace, the edge will chage 2 2 greatly, the weight of d( p ) is larger, the weight of 1 1 (, ) :( x, y ) :( x, y ) d p q is smaller, ad the the cotributio of edge cue to d( p :( x, ) is larger. Therefore, this adaptive itegratio method ca better reflect actual situatio durig tracig. 4.4 TEMPLATE ONLINE UPDATE I order to esure real-time tracig effect, this paper tracs the target by dividig the target ito fragmets, udges states of fragmets, ad adusts update strategy accordig to whether there is occlusio or appearace chage. The distace betwee the fragmet ad the template will be icreased because of occlusio or appearace chage. The template does ot have to be updated i the case of occlusio while the template eeds to be updated i the case of appearace chage. Fragmet weights of the template are give i Sectio 2. After the experimets we foud that if p 0.5, the q is a ivalid fragmet. Through the aalysis we foud that if the ivalid fragmet is caused by occlusio, the H compoet i HSV space of the fragmet will be distributed with greater probability i the previous frame i the bacgroud regio; if it is caused by target's ow chages, the H compoet i HSV space of the fragmet will be distributed with greater probability i the previous frame i the target regio. Because obstacles oly appear aroud the target, we select a "rig" bacgroud regio aroud the target to determie whether the target is occluded or ot, where the area of the aular regio is about 2 times of target area. The distributio of the H compoet i HSV space of the ivalid fragmet is bac proected oto the target ad aular bacgroud i the previous frame, so we ca obtai the distributio regio i the previous frame, ad the calculate respectively the probabilities of the ivalid fragmet belogig to the target regio ad the bacgroud regio. This paper uses the mai H compoet i HSV space of the ivalid 130
fragmet for bac proectio, ad the proectio method is the same as the oe used i Camshift [2]. As show i Figure 6, bac proectio image is a biary image, where white pixels deote the pixels whose grey values are equal to those of the mai H compoet of ivalid fragmets. I bac proectio image, we cout the umber of the foregroud pixels i the target area S ad the umber of 0 the foregroud pixels i the whole image S. If the t probability p of the H compoet of the ivalid 0 fragmet belogig to the target satisfies formula (16), we thi the target appearace chages. th is set to 0.8 after may experimets. If the ivalid fragmet is determied as occlusio, the template will ot be updated; if the ivalid fragmet is determied as appearace chage, the iformatio of the curret frame will be updated to the template. So that we ca rapidly get the target chage iformatio ad suppress bacgroud disturbace to the template at the same time, which obviously improves the tracig performace: S0 p0 log S S t 0 th. (16) The err( x, y ) is the locatio error betwee ob ob the target locatio ( x, y ) ad the true locatio ( x, y ). true true ob ob 5.1 THE QUALITATIVE ANALYSIS Test Sequece 1. Figure 7 shows the simulatio results of a video i a campus amed campus. The video sequece cotais 500 384 288-pixel colour images. I the video, from frame 272 to frame 377, the target eters ito dar regio from bright regio ad later returs to the bright regio. The illumiatio has a great chage. At frame 347, the tracig widow of the fragmet-based tracer loses the target; the colour-texture based meashift tracer does ot lose the target, but the estimatio locatio of the target has already deviated from the actual locatio. At frame 387, the tracig widow of the colour-texture based mea-shift tracer loses the target too while the tracig widows of our method ad the decetralized template tracer ever lose the target. FIGURE 6 Bac proectio images uder appearace chage or the occlusio 5 Experimets ad aalysis We use the followig four groups of experimets to verify the effectiveess of the algorithm. The experimetal eviromets are Microsoft visual studio 2008 ad Opecv2.0. The results are aalysed i Matlab 2008. Qualitative ad quatitative aalysis are made for the results of the four methods, the fragmet-based tracer [12], the colour-texture based mea-shift tracer [7], the decetralized template tracer [17], ad the method proposed i this paper (our method). I the figures, the widow of our method is labelled as 1, the tracig widow of the decetralized template tracer is labelled as 2, the tracig widow of the colour-texture based mea-shift tracer is labelled as 3, ad the tracig widow of the fragmet-based tracer is labelled as 4. The qualitative aalysis is doe through observig test images ad the quatitative aalysis is doe through measurig locatio errors. Let 2 2 ( xob xtrue ) ( yob ytrue ) err( xob, yob ). (17) 2 FIGURE 7 Tracig results of video Campus (Frames: 246, 272, 302, 347, 377, 387) Test Sequece 2. Figure 8 shows the simulatio results of video hall, which comes from CAVIAR database. The video sequece cotais 300 384 288- pixel colour images. The mai difficulty with this sequece is that the target is occluded by aother people. Whe the target is occluded at the video frame 191, both the fragmet-based tracer ad the decetralized template 131
tracer lose the target from frame 208 to frame 221, ad later o the colour-texture based mea-shift tracer loses the target too, while our method has ept tracig the target. To sum up, our method ca always trac the target o matter if there is occlusio or ot. The video sequece cotais 150 320 240-pixel colour images, where the ma s head i the sequece has irregular traslatio ad rotatio movemets. At frame 100 ad frame 110 whe the face is approximately frot face, the four methods trac the target accurately. Whe the bac face appears at frame 130, the fragmet-based tracer ad the colour-texture based mea-shift tracer lose the target immediately, ad the fragmet-based tracer caot recover from the error eve for a side face at frame 131. Our method ad the decetralized template tracer have more robustess. Test Sequece 4. Figure 10 shows the simulatio results of video ma2 which comes from SPEVI database. FIGURE 8 Tracig results of video hall (Frames: 163, 191, 208, 221, 240, 267) Test Sequece 3. Figure 9 shows the simulatio results of video ma1 which comes from SPEVI database. FIGURE 9 Tracig results of video ma1 (Frames: 100, 110, 117, 130, 141, 145) FIGURE 10 Tracig results of video ma2 (Frames: 368, 384, 398, 410, 474, 580, 590, 601) The video sequece cotais 600 320 240-pixel colour images. The target first rotates ad the the illumiatio chages sharply, followed by the part occlusio i the sequece. At frame 398 whe the target rotates, both the colour ad gradiet of the target chage, ad the fragmet-based tracer with oe sigle cue tracig loses the target whe the target turs bac, the colour-texture based mea-shift tracer with fixed template tracig loses the target too, ad the decetralized template tracer brigs about a certai tracig drift while our method achieves accurate 132
tracig. At frame 474, the lamp is tured o, which maes the illumiatio chage sharply ad the fragmetbased tracer lose the target agai, while the other three algorithms eep tracig the target. At frame 580 whe the target is occluded by aother movig obect, the other three algorithms fail to trac the target accurately while our method still tracs well, sice our method ot oly uses adaptive cue itegratio scheme durig fragmet tracig, but also employs the olie updatig template by distiguishig appearace chages ad occlusios. 5.2 THE QUANTITATIVE ANALYSIS Figures 7-10 show the qualitative aalysis results. We also provide the error plots for each sequece i Figure 11. FIGURE 11 Error plots for the video sequeces used i the quatitative aalysis It ca be see from these results that the proposed algorithm outperforms the fragmet-based tracer i terms of tracig accuracy. The reaso for this maily stems from our adaptive cue itegratio scheme, which removes the assumptio o the degree of potetial occlusios for the fragmet-based tracer, ad replaces its competitive approach to cue itegratio with a more cooperative strategy. The colour-texture based mea-shift tracer, which uses the oit colour ad LBP cues to trac target, outperforms the sigle cue tracig approaches ad is robust to the complex eviromet. However, it yields, i may cases, poor tracig results whe the target becomes occluded by the other obects i the scee. Our method ad the fragmet-based tracer cope with these ids of occlusio better tha most of the multi-cue itegratio tracers. The reaso stems from tracig the target i fragmets. The decetralized template tracer, which uses olie updatig template to trac target, is proved to be very robust agaist severe appearace chages sice it ca lear a ew obect model at each frame. Our method gives better results tha the decetralized template tracer for the video sequeces because of the fact that the decetralized template tracer updates target template at each frame without discrimiatio of appearace chages ad occlusios, ad caot prevet the template from driftig, eve if it uses short-term ad log-term appearace models As a result, for most of the video sequeces, our algorithm provides the best results. 6 Coclusios Video target tracig algorithm is faced with may problems, such as illumiatio chage, occlusios, appearace chages, etc. A ew tracig algorithm of multi-cue itegratio based o fragmet has bee proposed i this paper. The ew algorithm associates the image fragmets describig the differet parts of the target obect with multi-cue itegratio, where the weight of each cue is dyamically adusted durig tracig. I this way, the vote of each fragmet cotributes to the oit tracig result accordig to its adaptive weight itegratio. Furthermore, the algorithm uses the template updatig for target tracig, desigs the correspodig 133
template update algorithm, ad successfully implemets the target tracig uder complex bacgroud. We have demostrated the potetial ad the effectiveess of the proposed approach o various challegig video sequeces with differet tracig scearios. As revealed by our experimets, the proposed approach wors geerally better tha the fragmet-based tracer, the multi-cue itegratio tracers ad template-based tracers for the sequeces i the complex bacgroud of illumiatio, occlusio, ad the appearace chage. Acowledgemets The authors wish to tha the helpful commets ad suggestios from my teachers ad colleagues. This wor was supported by the Natioal Natural Sciece Foudatio of chia(grat No.31371533),Natioal Sciece ad Techology Support Program(Grat No. 2013BAJ10B12), he Key Techologies R & D Program of AHui Provice (Grat No.1301032169) ad by the Natural Sciece Foudatio of AHui Provice(Grat No.1308085MF89). Refereces [1] Birchfield S 1998 Computer Visio ad Patter Recogitio (CVPR) Sata Barbara, CA:IEEE Computer Society 232-7 [2] Triesch J, vo der Malsburg C 2001 Neural Computatio 13(9) 2049-74 [3] Yuru Wag, Xiaglog Tag, Qig Cui 2012 Patter Recogitio 45(12) 4510-24 [4] Brasett P, Mihaylova L, Bull D, Caagaraah N 2007 Image Visio Computatio 25(8) 1217-27 [5] Wag J Q, Yagi Y 2008 IEEE Trasactio o Image Processig 17(2) 235-40 [6] Jia G, Tia Y, Wag Y 2010 Dyamic multi-cue tracig with detectio resposes associatio Proceedigs of the ACM Multimedia NY USA: ACM New Yor 1171-74 [7] Nig J, Zhag L, Zhag D, Wu C 2009 Iteratioal Joural of Patter Recogitio ad Artificial Itelligece 23(7), 1245-63 [8] Li Yig, Liag Shi, Bai Bedu, Feg David 2012 Multimedia Tools ad Applicatios 6(3) 1-21 [9] Nehum S M S, Ho J, Yag M H 2008 Visual tracig with histograms ad articulatig blocs Proc. Cof. Computer Visio ad Patter Recogitio (CVPR), Achorage, AK :CVPR 2008 1-8 [10] Nicel K, Stiefelhage R 2008 Dyamic itegratio of geeralized cues for perso tracig Proc. Eur. Cof. Computer Visio (Frace) (ECCV): Marseille 514-26 [11] Chocaligam P, Pradeep N, Birchfield S 2009 Adaptive fragmets-based tracig of o-rigid obects usig level sets Proc. It. Cof. Computer Visio (Japa) (ICCV) :Kyoto 1530-7 [12] Adam A, Rivli E, Shimshoi I 2006 Robust fragmets-based tracig usig the itegral histogram Proc. Cof. Computer Visio ad Patter Recogitio (CVPR) 798-805 [13] Erdem E, Dubuisso S 2012 Computer Visio ad Image Uderstadig 116(7) 827-41 [14] Porili F, Tuzel O, Meer P 2006 Covariace tracig usig model update based o Lie algebra. Proceedigs of IEEE Computer Society Coferece o Computer Visio ad Patter Recogitio:New Yor, USA 728-35 [15] Collis R T, Liu Y X, Leordeau M 2005 IEEE Trasactios o Patter Aalysis ad Machie Itelligece 27(10) 1631-43 [16] Wag Yog-Zhog, Liag Ya, Zhao Chu-Hui, Pa Qua 2008 Acta Automatica Siica 34 (4) 393-9 [17] Firouzi Hadi, Naara Homayou 2012 Patter Recogitio 45(12) 4494-509 [18] Porili F 2005 Itegral histogram: A fast way to extract histograms i Cartesia spaces Proc. IEEE Cof. o Computer Visio ad Patter Recogitio (CVPR) 439-47 [19] Comaiciu D, Ramesh V, Meer P 2003 IEEE Trasactios o Patter Aalysis ad Machie Itelligece 25(5) 564-77 [20] Yag C, Duraiswami R, Davis L 2005 Fast multiple target tracig via a hierarchical particle filter Proceedigs of the Teth IEEE Iteratioal Coferece o Computer Visio: Beiig, Chia 212-9 [21] Simard R, L Ecyuer P 2011 Joural of Statistical Software 39(11) 1-18 [22] Fashig M, Tomasi C 2005 IEEE Trasactios o Patter Aalysis ad Machie Itelligece 27(3) 471-4 Authors Lichua Gu, October 1974, Sichua, Chia Curret positio, grades: Associate Professor of Computer Sciece Departmet of Ahui Agricultural Uiversity Uiversity studies: BSc, MSc degrees i computer sciece i HeFei Uiversity of techology, Chia, i 1997 ad 2005. Scietific iterest: research iterests, maily icludig machie learig, image processig, ad patter recogitio. Chegi Wag Curret positio, grades: master studies i agricultural iformatizatio i AHAU Uiversity Uiversity studies: BE i Logistics Egieerig, AHAU Uiversity (2013) Scietific iterests: research iterests iclude cogitive computig ad logistics system optimizatio Jiaxiao Liu Curret positio, grades: master studies i Agricultural Iformatics at Uiversity. Uiversity studies: BE i Logistics Egieerig (2013) from AHAU Uiversity. Scietific iterests: research directio icludes differet aspects of Agricultural logistics 134