Spatial Divide and Conquer with Motion Cues for Tracking through Clutter

Transcription

1 Spatal Dvde and Conquer wth Moton Cues for Trackng through Clutter Zhaozheng Yn and Robert Collns Department of Computer Scence and Engneerng The Pennsylvana State Unversty {zyn, Abstract Trackng can be consdered a two-class classfcaton problem between the foreground object and ts surroundng background. Feature selecton to better dscrmnate object from background s thus a crtcal step to ensure trackng robustness. In ths paper, a spatal dvde and conquer approach s used to subdvde foreground and background nto smaller regons, wth dfferent features beng selected to dstngush between dfferent pars of object and background regons. Temporal cues are ncorporated nto the process usng foreground moton predcton and moton segmentaton. Appearance weght maps talored to each spatal regon are merged and combned wth the moton nformaton to form a jont weght mage sutable for mean-shft trackng. Examples are presented to llustrate that dvde and conquer feature selecton combned wth moton cues handles spatal background clutter and camouflage well. 1. Introducton Persstent trackng of movng objects through changes n appearance s a challengng problem. To successfully handle appearance varaton, a tracker s object appearance model must be adapted over tme. However, adaptaton must be done carefully to avod drftng off the object. Common appearance-based trackng approaches such as mean-shft [6] and Lucas-Kanade [2] do not explctly model whch pxels belong to the object and whch belong to the background. As a result, t s easy for pxels n the background to be mstakenly ncorporated nto the object appearance model, thus contrbutng to tracker drft. Fgure-ground separaton s emergng as a key technque for drft-resstant trackng. By explctly separatng object pxels from background, a tracker can adapt to object and background appearance changes separately, and n a prncpled way. Common methods for fgure-ground separaton nclude moton segmentaton [12] and actve contours [8]. More recently, fgure-ground separaton for trackng has been addressed as a two-class classfcaton problem, where object pxels must be dscrmnated from background pxels based on local mage cues such as color or texture. Wth the realzaton that trackng can be formulated as dscrmnatve fgure-ground classfcaton comes a growng realzaton that choce of features for separatng object from background s also mportant. By usng trackng features that clearly separate object from background classes, the tracker s much less lkely to drft off the object onto smlar background scene patches. In ths paper we consder the problem of choosng features that dscrmnate between object and background. We partcularly focus on cases wth background clutter and camouflage, where t s dffcult to acheve good fgureground separaton usng only a sngle feature. Related Work Our work s most closely related to Collns et.al. [5] and Avdan [1]. In [5], samples of pxels from the object and background are analyzed to perform on-lne selecton of dscrmnatve features to use for trackng. The varance rato s used to rank each feature by how well t separates emprcal dstrbutons of object and background feature values. Features that maxmze average separablty between the foreground object and the entre surroundng background are ranked most hghly by ths approach, thus s best suted to backgrounds that are relatvely unform n appearance. Avdan [1] mantans an ensemble of weak classfers combned va Adaboost to perform strong classfcaton of foreground from background pxels. Each weak classfer s a least-squares lnear decson functon (hyperplane) n the raw, mult-dmensonal feature space. Ths approach s slower than hstogram-based methods, but generalzes to hgh-dmensonal feature spaces. Note that t s a feature weghtng approach, as opposed to feature selecton, whch ams to choose a lower-dmensonal subset of features. Lke [5], ths method also dscards spatal nformaton that could be used to reason about the layout of clutter and dstractor objects. In ths paper, we consder an explct approach to deal wth spatal layout of background clutter, dstractors, and camouflage. We explore the dea that dfferent features may be necessary to dscrmnate between the object and dfferent portons of the scene background. For example, consder trackng a car from an aeral vew. One feature may dstngush well between the car and the road n front and behnd t, a second may be better at dscrmnatng between the car and folage at the sde of the road, whle yet a thrd feature may be needed to separate the car from a vehcle of nearly the same color passng t on the left. We generalze ths dea nto a dvde-and-conquer strategy that spatally decomposes the background nto cells

2 whle choosng a feature for each cell that best dscrmnates t from the foreground object. Lke [1] [5], we perform a soft object-background classfcaton by formng a weght mage representng lkelhood that each pxel belongs to the object versus the background (pxels more lkely to be object have hgh weght, whle pxels more lkely to be background have low weght). Foreground moton predcton and moton segmentaton are fused wth the appearance weght mage to form a jont color-moton weght mage. Mean-shft s then performed on the jont weght mage to fnd the local mode of object locaton. More sophstcated technques based on statstcal samplng could be used to robustly fnd the mode [11]. 2. Measurng Feature Separablty When seekng good trackng features, we would lke smple features that relably separate the object from the background. Smlar to [5], we use raw features chosen from a set of lnear combnatons of RGB values. Each feature s normalzed nto the range of 0 to 255 and then quantzed nto hstograms of 2b bns. Other cues could be used n the feature selecton process, ncludng edge orentatons, shape contexts, texture features and flow Extended Varance Rato An evaluaton crteron s needed to select the best features from the canddate set. Features that produce separable object and background class dstrbutons should score most hghly. For unmodal dstrbutons, the varance rato s a good measure of separablty. Gven a feature, let () be the hstogram on the object and () H bg H obj be the hstogram on the background. We normalze to form probablty densty functons of object and background, p() and q (). The overall combned densty of the object and background s then p( ) + q( ) p tot = (1) 2 The orgnal varance rato s defned n terms of the raw object and background dstrbutons as var( ptot ) VR( = (2) var( p) + var( where var(p) denotes the varance of a dstrbuton p. The ntuton behnd the varance rato s to select features that maxmze the dfference between object and background classes whle mnmzng the varaton wthn each class. To evaluate separablty of multmodal dstrbutons, we adopt an extended varance rato crteron [5]. A log lkelhood transformaton 1 1 In practce, ths functon s modfed to avod dvdng by zero or takng the log of zero. Ths modfcaton s omtted here, for clarty. p( ) L ( ) = log (3) q( ) nonlnearly maps raw feature values nto a new feature space such that values that appear more often on the object map to unmodal postve values, and values that appear more often on the background map to unmodal negatve values. The varance rato s then appled to ths new log lkelhood feature to evaluate separablty of the orgnal raw feature dstrbutons. Ths can be thought of n terms of an extended varance rato var( L; ptot ) EVR( L; = (4) var( L; p) + var( L; where var(l;p) denotes the varance of log lkelhood functon L wth respect to dstrbuton p [9] Comparng Separablty Measures One queston to ask s whether the extended varance rato s the best measure of separablty of two dstrbutons, or whether alternatve nformaton theoretc measures lke KL dvergence mght be more approprate. In ths secton we compare the extended varance rato to KL dvergence, cross entropy and a measure smlar to mutual nformaton. The Kullback-Lebler dvergence or relatve entropy s a measure of the dfference between two probablty dstrbutons; however t s not symmetrc and does not satsfy the trangle nequalty. The KL dvergence between p() and q() s defned as p KL( = p log (5) q The cross entropy measures the overall dfference between two probablty dstrbutons, whch s defned as H ( = p log q (6) It can be seen from the defntons of (6) and (7) that H ( = KL( + H ( p) (7) where H ( p) = plog p (8) To make the KL measure and the cross entropy be symmetrc measures, we modfed them as: = p + q KL' ( plog qlog (9) q p H '( = plogq qlog p (10) so that the modfed KL dvergence and cross entropy have the followng relaton: H '( = KL' ( + H ( p) + H ( (11) The last measure we evaluate s smlar n form to mutual nformaton, but t quantfes the dstance between overall dstrbuton p and the product of p() and q () : tot p I ( p, q ) = tot p log tot p q (12)

3 To evaluate these measures of separablty, we tested them on a seres of generated dstrbutons. Table 1 shows the performance of the four crtera n two sets of tests. In the frst test, the means of two unmodal dstrbutons (Gaussans) are brought closer and closer together. The dstrbuton labeled P obj1 s most separable from the background dstrbuton. When the means of the dstrbutons of the object and background get closer, they become less separable. We see that the quantty of the extended varance rato measure decreases faster (from 29.2 to 1.2) than the other three measures. In the second test, the background s a bmodal dstrbuton P bg, and three unmodal Gaussans wth dfferent means are compared. Based on the extended varance rato measure, P obj2 s judged the most separable from the bmodal background. Moreover, the dfference between the separablty scores of P obj1 and P obj2 s not very large, correspondng to vsual ntuton that both cases are equally separable from the background. However, based on the other three measures, P obj2 s rated worst among the three features, whch s not correct snce dstrbuton P obj2 s clearly more separable from P bg than P obj3 s. Obj1 Obj2 Obj3 Obj1 Obj2 Obj3 EVR 29.2* * 2.2 I 27.5* * KL 63.9* * H 56.4* * Table 1: For each crteron, * represents the best feature choce (judged most separable by the measure) and the shaded one represents the worst feature choce (judged least separable). Durng the smulaton, many other cases also showed that the extended varance rato crteron performed best for measurng separablty of both unmodal and multmodal dstrbutons [13]. We beleve the reason s that the other three measures only evaluate the dfference between two dstrbutons, whle gnorng the varance property of each ndvdual dstrbuton. 3. Dvde and Conquer Approach Many smple classfcaton algorthms such as LDA assume the underlyng class dstrbutons are unmodal. However, when extractng a background hstogram from the pxel neghborhood surroundng an object, we often get a multmodal dstrbuton due to scene clutter. A second problem to address s nearby confusors havng smlar appearance to the foreground object. Such confusors typcally have lmted spatal extent, yet are hghly lkely to cause trackng falure. We beleve that spatal reasonng s mportant for solvng the problems of clutter and confusors. However, the necessary spatal nformaton s dscarded by the background hstogram representaton. To solve ths problem, we use a spatal dvde and conquer approach, as llustrated n Fgure 1. Lke prevous approaches, we select features based on the prevous frame and use them to calculate the weght mage of the current frame for trackng. However, frst the object and the background n the prevous frame are decomposed nto smaller regons (cells). The dea s that the class dstrbutons of these smaller spatal cells should be more easly separable. Guded by the extended varance rato crteron, the best feature for each parng of object cell and background cell s chosen. In other words, dfferent features can be chosen for dscrmnatng between dfferent regons of the object and the background. Pérez et.al use a mult-regon reference model for color based trackng [11]. In ths paper we dvde the background and foreground regons adaptvely and recursvely. Features from each object-background cell parng should produce weght mages that dscrmnate well between the correspondng spatal regons of foreground and background. All weght mages are then merged together to generate a sngle weght mage that acheves good separaton of the entre foreground and surroundng background. Ths weght mage s used for mean-shft trackng. Fgure 1: In the prevous frame the object has one unmodal cell and the background s dvded nto 8 spatal cells. For each of the 8 parngs of object to background cells, the best feature s selected and a correspondng weght mage s generated on the current frame. The 8 weght mages are then merged together nto a sngle weght mage for trackng Dvde and Conquer Image segmentaton could be used to break the object and the background nto regons wth unmodal feature dstrbutons, but ths approach would be too slow for an on-lne trackng process. In fact, we only need to estmate the prncpal unmodal dstrbutons n feature space, rather than analyzng the exact shape or contour of each background regon. We therefore hypothesze that a coarse spatal decomposton s suffcent to provde resstance to background clutter and confusors.

4 Fgure 2 llustrates the dvde-and-conquer feature selecton process. A grey car wth a roughly unmodal dstrbuton s tracked. There are many ways to spatally dvde the background. In ths example the background around the car s dvded nto 8 neghborng regons as shown n the center of Fgure 2(a). Also shown are the object-background class dstrbutons for the feature havng hghest extended varance rato score for each cell. features can separate the object from one correspondng background cell qute well (object regon has hgh weght and background regon has low weght), but that each feature does not guarantee good separaton between the object and other background cells. For example, the feature n the lower left mage n Fgure 2(b) dscrmnates the car from ts lower left background well, but does a poor job at dstngushng the car from the rght-sde background. If the best feature for a cell n ths ntal dvdng step results n unmodal class dstrbutons, the feature s accepted. Otherwse we subdvde agan untl unmodal dstrbutons are acheved. For example, the left-sde background regon n Fgure 2 has a bmodal dstrbuton even usng the best feature, and s therefore further dvded nto four subregons as shown n Fgure 3. We stop dvdng when unmodal dstrbutons are acheved or when the number of pxels n a regon becomes too small. (a) (a) (b) Fgure 2: (a) The background around the car s dvded nto 8 spatal cells (the car has a roughly unmodal dstrbuton wthout dvdng). For each parng of background regon and object, a feature that generates the most separable class dstrbutons s chosen. These class dstrbutons are shown for each cell, along wth the correspondng Kurtoss values. (b) The weght mages based on the best feature selected for each cell. In Fgure 2(b), the correspondng weght mages g ( x, for each cell are shown. These weght mages are formed from the log lkelhood values L (eq. 3) computed from the class dstrbutons nduced by the selected feature for each cell. If we were to threshold any weght mage at zero, t would be equvalent to performng a bnary classfcaton of object from background at each pxel usng the lkelhood rato test on the class condtonal dstrbutons. From the weght mages, we can see that the 8 selected (b) Fgure 3: (a) The left-sde background regon s dvded nto four sub-quadrangles. (b) Object/background feature dstrbutons and correspondng weght mages, based on the best features found for each subregon. To decde f a dstrbuton s sutably unmodal, we use the Kurtoss measure: E[( x µ ) 4 ] k = (13) 4 σ The kurtoss of the normal dstrbuton s 3. Dstrbutons that are sharper than the normal dstrbuton have kurtoss value greater than 3; dstrbutons that are flatter have kurtoss less than 3. We decde to further dvde a regon when the kurtoss value s smaller than 2. The EVR value can also be used to decde whether to contnue dvdng Merge After obtanng the weght mages for each objectbackground cell par, we merge them together nto a sngle weght mage. Rather than just cut and paste

5 regons from each weght mage, we smoothly nterpolate accordng to: W ( x, = c V ϕ ( x, (14) wherev s the area n pxels of the background regon, c s the extended varance rato score, and ϕ ( x, s an enhanced weght mage for the object/background parng, as defned by: ϕ ( x, = αg ( x, + (1 α ) g ( x, (15) ( x, y ) obj or ( x, y ) bg ( x, y ) obj and ( x, y ) bg α (0.5,1] The ntuton behnd enhanced weght mages s that each weght mage only dscrmnates well n the specfc spatal cells that the weght mage was derved from, thus should contrbute relatvely more to the output values of pxels n the same cell locatons n the fnal merged weght mage, and relatvely less to pxels n other cells. Fgure 4 shows the merged weght mage for trackng, usng a reweghtng parameter of α = The object s well dstngushed from the entre local background surroundng t. We also show the result after smoothng wth a Gaussan flter. It s known that mean-shft performs hll-clmbng wthn ths fltered space [4], thus the fact that there s a sngle strong mode at the locaton of the foreground object ndcates that ths s a good weght mage for mean-shft trackng. Fgure 4: (left) Fnal merged weght mage for trackng. The car s well-dstngushed from the surroundng background. (rght) Gaussan flterng of the weght mage shows a sngle strong mode, ndcatng that mean-shft wll successfully fnd the object locaton n ths frame Dfferent Spatal Dvdng Methods Dfferent subdvsons of the scene background wll generate dfferent weght mages for trackng. Fgure 5 shows two alternatve ntal spatal dvsons and ther correspondng weght mages. The top one consders only the foreground regon versus the entre surroundng background, whch s equvalent to the method of [5]. The bottom mage subdvdes the background nto four regons correspondng to front, back, left and rght, wth respect to drecton of object moton. We see that even ths coarse dvson nto four spatal regons yelds a large mprovement. Comparng wth Fgure 4 shows that ncreasng levels of subdvson yeld even better weght mages, although they are more costly n terms of computaton tme. Fgure 5: Alternatve ntal subdvsons. Top row: usng a sngle surroundng background regon. Second row: dvdng the background nto 4 peces Moton Estmaton Moton estmaton provdes a powerful cue for detecton and segmentaton of movng objects. We use moton estmaton for two tasks. Frst, we perform frame dfferencng to determne movng pxels from statonary scene background. Ths coarse moton segmentaton s fused wth the weght map produced by color appearance nformaton to help track the object n camouflage stuatons and to avod drft onto statonary background structures. Second, we perform constant velocty predcton of object moton based on prevous locaton hstory to predct future object trajectory. The predcted locaton of the object n the next frame s used to center the grd of spatal cells for weght mage constructon n that frame, and to form a gatng regon [3] wthn whch to search for the object. The predcted future object trajectory also helps track through temporary occlusons. However, we consder vdeos where the camera can be movng, and both constant velocty trajectory predcton and frame dfferencng are nvald under camera moton. For ths reason, we use estmates of frame-to-frame background moton to compensate for apparent camera moton when applyng constant velocty moton predcton, and to perform stablzaton when segmentng moton va frame dfferencng. Two-frame background moton estmaton s performed by fttng a global parametrc moton model (affne or projectve) to sparse optc flow. Sparse flow s computed by matchng Harrs corners between frames usng normalzed cross correlaton. A good set of potental matches s foundng usng marrage-based compatblty tests, as descrbed n [10]. Gven a set of potental corner correspondences across two frames, we use a Random Sample Consensus (RANSAC) procedure [7] to robustly estmate global affne flow from observed dsplacement vectors. The largest set of nlers returned from the RANSAC procedure s then used to ft ether a 6

6 parameter affne or 8 parameter planar projectve transformaton. The object moton predcton module assumes the object travels wth constant velocty wthn three consecutve frames after frst compensatng for the background moton. It provdes a rough locaton for ntally subdvdng the object and background. The tracked object can move wth varyng speed and drectons n the real world. For example, assumng an affne model of background moton, the formula to predct the locaton Pt of the object n the current frame t, gven ts prevously observed postons Pt-1 and Pt-2 n the last two frames, s t 1 t 2 P T P + P T P (16) [ ( ( )] t = t t 1 t 1 t 1 * t 2 where T t 2 t 1 s the affne moton between frames t-2 to t-1, t 1 and Tt s the affne moton between frames t-1 and t. Sample results of object locaton predcton and moton detecton are llustrated n Fgure 6. Moton detecton weghts (Fgure 6c) and the appearance weght mage (Fgure 6d) are fused to generate a better weght mage for trackng (Fgure 6e). Currently, a logcal and operaton s used for fuson such that a pxel n Fgure 6e s vald only when the pxel value s larger than some threshold and moton exsts at that pxel. Fgure 6: (a) prevous frame. (b) current frame. The predcted object locaton s showed as a dashed ellpse. The search regon s the enlarged ellptcal area around the predcted locaton. (c) moton mask computed by frame dfference between the current frame and the warped prevous frame. (d) appearance weght mage by dvde and conquer approach. (e) weght mage fused wth the moton nformaton. (f) mean-shft trackng result on the jont color-moton weght mage Trackng Algorthm Due to the contnuous nature of vdeo, the dstrbuton of the object and the background should reman almost the same between two successve frames. Thus t s reasonable to select features from the prevous frame and apply them to the current frame to generate a weght mage for trackng. The object s then localzed n the weght mage usng mean shft, and the orentaton of the object s determned by weghted ellpse fttng. We ncorporate the dvde and conquer feature selecton approach nto the followng trackng algorthm: Intalzaton: 1) Manually select foreground object n the frst frame. 2) Automatcally dvde the object/background nto small regons and select the best feature for each objectbackground par. If the features dstrbutons of object and background satsfy the Kurtoss threshold or the object/background regon s too small, the feature s accepted and subdvson s stopped. Otherwse, contnue to dvde and conquer the feature selecton. Trackng: For subsequent frames, do: 1) Predct the target poston usng background moton estmaton and constant velocty foreground moton predcton. 2) Generate weght mages from cell layout around the predcted locaton usng the best selected features assgned to the dfferent spatal regons n the prevous frame. Merge the weght mages together to generate an appearance weght mage. 3) Compute a moton detecton mage usng moton compensated frame dfferencng between the warped prevous frame and current frame. 4) Use mean shft to fnd the nearest local mode n a combned weght mage contanng both appearance generated weghts (from step 2) and moton detecton weghts (from step 3). 5) Ft an ellpse centered on the object locaton determned by mean-shft to get the orentaton and scale of the object for the next round of dvde and conquer. 6) Update the spatal cell layout and selected trackng features from the current frame usng the dvde and conquer approach. Note that step 4 uses both appearance-based and moton-based weghts. Ths further mproves trackng performance by ensurng that the tracker s not dstracted by statonary regons n the background, regardless of how smlar they may appear to the tracked object. The jont color-moton weght mage also keeps the tracker away from dstractng moton near the target.

7 4. Experments In ths secton we present three challengng trackng examples that llustrate the benefts of spatal dvde and conquer wth moton cues for trackng through clutter. The frst aeral vdeo shows a grey car chased by the polce, from the VIVID Trackng Evaluaton Webste ( Durng the chase, the car passes cars of smlar color, s partally occluded by traffc sgns, and passes through shadows. Camera moton s also promnent, ncludng scale changes and rotaton. Fgure 7 shows sample frames from the sequence. The frst row shows the tracked object n the orgnal mage. The second row shows weght mages generated by the dvde and conquer approach. Spatal decomposton as descrbed above s used to mnmze the appearance of clutter and dstractons n the weght mage. The thrd row llustrates moton detecton results. Note that moton detecton can add new clutter, as shown n the last column of Fgure 7 where two cars move n parallel. However, usng both the appearance weght mage and moton detecton results, the tracker succeeds. The second vdeo s from the OTCBVS Benchmark Dataset Collecton ( Fgure 8 shows the trackng result wth the weght mages and moton results. The object beng tracked s a pedestran walkng from sunshne nto a movng cloud shadow. The pedestran also becomes partly occluded and separated nto peces by a sculpture. Nonetheless, the pedestran s successfully tracked untl they leave the vew. Note that the pedestran gat and body shape s very clear n the weght mages. The thrd experment s an aeral vdeo sequence of a motorcycle (Fgure 9). The motorcycle s small and fast movng, and often passes vehcles wth smlar colors. Sometmes the object s runnng through or passng by shadows. Sometmes t s partal occluded by traffc sgns or nearby vehcles. Agan, we see that the weght mages produced by the dvde and conquer approach are qute good for dstngushng the motorcycle from ts surroundng background, and that moton detecton results alone would not succeed, due to moton clutter. 5. Summary Ths paper presents a dvde and conquer approach to consder the spatal layout of background clutter wth respect to the foreground object. Guded by an extended varance rato crteron for determnng separablty of dstrbutons, features are selected that dscrmnate between spatal pars of object and background cells. Weght mages wth good dscrmnatve qualty are generated for each cell, and merged to produce a sngle weght mage for mean shft trackng. A moton predcton module allows for reduced object search regons and moton detecton va compensaton of background camera moton. The mean-shft procedure thus consders both appearance and moton based weght mages to determne object locaton n a new frame. After fndng the locaton of the object, weghted ellpse fttng s executed to fnd the object orentaton. The experments show good performance on vdeo scenes contanng spatal clutter, dstractons and camouflage. Future work wll more drectly address the problem of drft durng object appearance adaptaton. Although spatal appearance and temporal moton nformaton are combned n the present algorthm, we stll need to evolve the object model carefully to avod drft. Future work wll focus on mposng shape-guded foreground/background segmentaton nto the trackng process, to avod model drft durng adaptve trackng. Acknowledgements Ths work was funded under the NSF Computer Vson program va grant IIS on Persstent Trackng. References [1] S.Avdan, Ensemble Trackng, IEEE Conf on Computer Vson and Pattern Recognton (CVPR), 2005,pp [2] S.Baker and I.Matthews, Lucas-Kanade 20 Years On: A Unfyng Framework, Internatonal Journal of Computer Vson, 56(3): , March, [3] S.Blackman and R.Popol, Desgn and analyss of Modern Trackng Systems, Artech House, Norwood MA., [4] Y.Cheng, Mean Shft, Mode Seekng, and Clusterng, IEEE Trans on Pattern Analyss and Machne Intellgence, 17(8): , August [5] R.Collns, Y.Lu and M.Leordeanu, Onlne Selecton of Dscrmnatve Trackng Features, IEEE Pattern Analyss and Machne Intellgence, 27(10): , Oct [6] D.Comancu, V.Ramesh, and P.Meer, Kernel-based Object Trackng, IEEE Transactons Pattern Analyss and Machne Intellgence, 25(5): , May [7] M.Fschler and R.Bolles, Random Sample Consensus, Communcatons of the ACM, 24(6): , [8] M. Isard and A. Blake, CONDENSATION - Condtonal Densty Propagaton for Vsual Trackng, Internatonal Jounal of Computer Vson, 29(1):5-28, 1998 [9] A.Karr, Probablty (Sprnger Texts n Statstcs), Sprnger Verlag, 1996, ISBN: [10] D.Nster, O. Narodtsky, and J.Bergen, Vsual Odometry, IEEE Conf on Computer Vson and Pattern Recognton, , June [11] P. Pérez, J. Vermaak, A. Blake. Data fuson for vsual trackng wth partcles. Proc. IEEE, 92(3): , [12] H.Tao, H. Sawhney and R. Kumar, Object Trackng wth Bayesan Estmaton of Dynamc Layer Representatons, IEEE Transactons Pattern Analyss and Machne Intellgence, 24(1): 75-89, January 2002 [13] Z. Yn and R. Collns, Spatal Dvde and Conquer wth Moton for Trackng, Tech Report CSE , Penn State Unversty, 2005

8 Fgure 7: A car passes smlar cars, partally occlusons, moves through shadows and turns a corner. Fgure 8: A pedestran walks nto a movng shadow and s partally occluded by a sculpture. Fgure 9: A fast-movng motorcycle passes vehcles of smlar color, partal occlusons, and moves through shadows.