Real-time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes

Transcription

1 Real-tme Multple Obects Tracng wth Occluson Handlng n Dynamc Scenes Tao Yang 1, Stan Z.L 2, Quan Pan 1, Jng L 1 1 College of Automatc Control, Northwestern Polytechncal Unversty, X an, Chna, Natonal Lab of Pattern Recognton, Insttute of Automaton, Chnese Academy of Scences, Beng, Chna, yangtaonwpu@163.com, szl@nlpr.a.ac.cn, uanpan@nwpu.edu.cn, nglnwpu@163.com Abstract Ths wor presents a real-tme system for multple obect tracng n dynamc scenes. A unue characterstc of the system s ts ablty to cope wth longduraton and complete occluson wthout a pror nowledge about the shape or moton of obects. The system produces good segment and tracng results at a frame rate of fps for mage sze of 320x240, as demonstrated by extensve experments performed usng vdeo seuences under dfferent condtons ndoor and outdoor wth long-duraton and complete occlusons n changng bacground. 1. Introducton 1 Obect tracng s an essental component of an ntellgent vdeo survellance system. Accurate and real tme obect tracng wll greatly mprove the performance of obect recognton, actvty analyss and hgh-level event understandng [1,2,3]. The most common approach to tac obects s to frst detect them usng bacground subtracton and, then, establsh correspondence from frame to frame to fnd the tracs of the obects [4,5]. Despte ts popularty, bacground subtracton based methods stll lac the robustness to handle specfc events such as tracng multple nteractng obects wth heavy occluson, the most unwanted events that often happen n vdeo. In vdeo seuences, these nteractons result n several challenges to the tracng algorthm. Snce blob generaton of movng obects s based on connected component analyss, close obects generate a sngle merged obect, and n ths stuaton, vsual features of the occluded obects are not observed and occluded obects can not be traced Related Wor Methods to solve the occluson problem n multple nteractng obects tracng have been prevously presented. Shloh [6], Chang [7] and Docstader [8] 1 The wor presented n ths paper was sponsored by the Foundaton of Natonal Laboratory of Pattern Recognton and Natonal Natural Scence Foundaton of Chna ( ) overcame occluson n multple obect tracng by used fusng multple camera nputs. Cucchara [9] proposed probablstc mass and appearance models to cope wth freuent shape changes and large occlusons. ng [10] developed a Bayesan segmentaton approach that fused a regon-based bacground subtracton and a human shape model for people tracng under occluson. Wu [11] proposed a dynamc Bayesan networ whch accommodates an extra hdden process for partal occluson handlng. Andrew [12,13] used appearance models to trac occluded obects. Sebel [14] proposed a tracng system wth three co-operatng parts: an actve shape tracer, a regon tracer and a head detector. The regon tracer explots the other two modules to solve occlusons. Heu [15] proposed a template matchng algorthm and update the template usng appearance features smoothed by alman flter. Tao[16] presented a dynamc bacground layer model and model each movng obect as a foreground layer, together wth the foreground orderng, the complete nformaton necessary for relably tracng obects through occluson s ncluded. Alper [17] traced the complete obect and evolvng the contour from frame to frame by mnmzng some energy functons. Although many algorthms have been proposed n the lterature, the problem of multple nteractng obects tracng n complex scene s stll far from beng completely solved. Multple camera based tracng methods [6,7,8] cannot handle complete occluson. Precse model based algorthms [12,13] are senstve to bacground clutter, and they are at the cost of computatonally more expensve schemes because model estmaton for the number of model parameters s usually large. Moreover, many of those algorthms are desgned to deal wth short-duraton partal occluson, and fal at severe and complete occlusons and when a partal occluson lasts for a long tme. Probablstc approaches le Monte Carlo flter s useful n dealng wth the problem of bacground clutter as t allows for the tracng of multple hypotheses [18,19,20]. However, the measure of obect has to be detected by an ndependent technue that may not be acured n heavy occluson. Several methods usng moton model to perform robust tracng can deal wth some nstances of occluson. These methods reure precse moton modelng [21] and fal at the nonlnear moton of nteractng obects.

2 1.2 Our Methods To deal wth multple obect tracng n dynamc scenes, we separate the obect state nto three parts: Before, durng and after occluson. Consderng occluson often cased by touchng obects, we suppose that durng the occluson, the traectory of each ndvdual obect s smlar to the entre group, fortunately, ths s a vald assumpton n real survellance scene. If we could eep on tracng and labelng each ndvdual obect correctly before and after the occluson, and tracng the entre group durng the occluson, the ntegrate traectory of each obect wll be recovered. To develop such nd of real-tme multple obects tracng system, several problems have to be consdered. The frst s fast and accurate obect segmentaton. Precse segmentaton result s the bass for obect feature extracton and further data assocaton. However, problems such as ghost, left obect, uncertanty camera shang, abrupt llumnaton changes would brng great challenges to ths problem. Another problem s how to detect the occluson and splttng events robustly. Consderng the complex scenes and the noses, a blob s often erroneously splt nto several parts, and t s dffcult to decde whether those parts belong to the same obect or should splt from a group. Moreover, nose also deterorates decson result. In ths paper, we present a real-tme system for multple obect tracng n complex real world. A unue characterstc of the system s ts ablty to cope wth longduraton and complete occluson n dynamc scenes, and unle other systems, ths s acheved wthout pror nowledge about the shape or moton of obects Outlne of the System The system conssts of two parts (shown n Fgure 1): (1) obect segmentaton, and (2) mergng and splttng detecton, and feature correspondence. In part one, a fast algorthm s presented for bacground mantenance to handle varous scene changes, ncludng ghosts and llumnaton changes, runnng at 20 fps. The nput vdeo s used to estmate a bacground model based on a two level pxel moton analyze algorthm, whch s then used to perform bacground subtracton mage. After connected area analyss, small blobs wll be removed and the resultng foreground regons wll be saved. To reduce the large scale noses caused by bacground clutter, the tracng management module of the second part assocates foreground regons n consecutve frames to construct hypotheszed tracs, only those blobs whch have been correctly corresponded for several frames wll be consdered as a vald target. Fgure 1. Tracng System Dagram. Part 1: Bacground Mantenance and Movng Obect Segmentaton. Part 2: Obect Tracng and Occluson Handlng. In part two, a combnaton mechansm s embedded to detect mergng and splttng events, usng obect tracng and segmentaton result. In the mergng and splttng detecton module, the detected obect s dvded nto four classes: exstng obect, new obect, merge obect and splt obect. The frst two class obects wll be drectly used to update the tracer n the tracng management module. For the merge obect, a group wll be created whch contans the traectory and color feature of the obects n t. For the splt obect, the feature correspondence module are employed to assgn a correct label to each splt obect based on Kullbac-Lebler (KL) dstance. In the followng, we explan detals of the system. xtensve experments wth vdeo seuences under dfferent condtons ndoor and outdoor show that the system s effectve and effcent n multple obect tracng n complex scenes. It s accurate yet hghly computatonally effectve.

3 The paper s structured as follows: Secton 2 presents the movng obect segmentaton algorthm. Secton 3 explans the mergng and splttng detecton method. Secton 4 dscusses feature correspondence method. Secton 5 presents extensve results. 2. Movng Obect Segmentaton We adopt the bacground subtracton approach. However, rather than based on mxture Gaussan models [5,22] for bacground modelng or relyng upon the dstrbuton of the pxel value, we present a two level (pxel level and frame level) bacground mantenance algorthm for real-tme segmentaton and bacground updatng. Ths s to avod problems (hgh computatonal costs and slow adaptaton to a new bacground model) assocated wth mxture Gaussan bacground modelng. The basc dea of the pxel level bacground updatng s based on an assumpton that the pxel value n the movng obect s poston changes faster than those n the real bacground. Fortunately, ths s a vald assumpton n most applcaton felds. Under ths assumpton, we can dstngush the foreground and bacground accurately by a smple frame-to-frame dfference method, whch could detect the fast changes of pxel. However, ths method wll fal when the nsde color of obect s unform. In ths stuaton, pxel values do not vary wthn the obect. To deal wth ths problem, we present a dynamc matrx D() to analyzng the changes detecton result of the frame-to-frame dfference method, where the moton state of each pxel s stored n the matrx. Only those pxels whose values do not change much can be updated nto the bacground. Let I () denotes the nput frame at tme, and the subscrpt, of I, ( ) represent the pxel poston. The frame-to-frame dfference mage F() and the dynamc matrx D () at tme are defned as follows:. 0 I, ( ) I, ( γ ) Tf F ( ) = (1), 1 otherwse D, ( 1) 1 F, ( t) = 0, D, ( 1) 0 D, ( ) = (2) λ F, ( t) 0 where γ represent the nterval tme between the current frame and the old one, Tf s the threshold to mae a decson whether the pxel s changng at tme or not, and λ s the tme threshold of the pxel s stable tmes n consecutve frames. Once D, ( ) euates to zero, the pxel wll be updated nto the bacground wth a lnear model : B, (,, ) = α I ( ) + (1 α) B ( 1) (3) where B () s the bacground mage at tme and α s the weght of nput frame. Although the pxel level bacground update method could deal wth many serous problems mentoned above, t stll has a drawbac n that t only consders each ndvdual pxel whle gnorng the moton nformaton contaned n the frame. The frame level updatng s used to solve ths problem. The mechansm utlzes the movng character of the whole mage ν (4) to acheve fast bacground update under the stuaton of the abrupt scene changng such as camera shang, llumnaton changng and new left obect n the scene. n m F, ( ) = = ν = m n (4) where m, n represent the wdth and heght of the mage. Once v s less than a threshold, we wll mae a decson that no movng obect n the current mage and update all the stable pxels n the current frame to the bacground mmedately usng (3). By fusng the detecton result at both pxel and frame levels, the bacground update procedure mantans a sutable bacground model under dfferent condtons. In bacground subtracton step, each vdeo frame s compared wth the reference bacground model, pxels n the current frame that devate sgnfcantly from the bacground wll be detected. After the real tme obect segmentaton based on connected blob extracton and mage down samplng, a sze flter s used to remove small components and the movng obect postons wll be ganed and transformed to the orgnal subtracton mage to get the accurate fnal segmentaton results. Consderng the bacground clutter and the smlarty of the foreground regon and the bacground, noses blobs wth large sze are stll exst after the morphology flterng a b c d Fgure 2. An example of obect segmentaton. a) Input vdeo. b) Subtracton Image. c) Morphology flterng result. d) Fnal result fused wth spato-temporal nformaton.

4 (Fgure 2.c). To solve ths problem, we analyze the correct correspondence tmes of each blob n consecutve frames, thus uncertan noses wll be removed. Through fuson the spato-temporal nformaton of each segmented blob, a precse obect can be acured (Fgure2.d). 3. Mergng and Splttng Detecton Ths module ncludes two man steps: (1) correspondence between foreground regon and trac, and (2) mergng and splttng detecton. As n most of the tracng approaches, the correspondence process attempts to assocate the foreground regons wth one of the exstng tracs. Let T ( ) = { T1 ( ), T2 ( ),..., Tm ( )} denotes the exstng tracs and M ( ) = { M 1( ), M 2 ( ),..., M n ( )} denotes the foreground regon measures at tme. Ths process starts wth the constructon of a dstance matrx D between the actve tracs T () and the each of the foreground regon measure M (). The dstance matrx D (rows correspond to exstng tracs and columns to foreground regons n the current frame) s based on the ucldean dstance(5). D (, ) = T ( ) M ( ) 2 2 = ( Tx ( ) M x ( )) + ( Ty ( ) M y ( )) (5) where T x, Ty, M x, M y represent the center postons of the boundng box of T and M, = 1, K, m, = 1, K, n. Consderng the smlarty between the tracer and measure, f ther dstance s larger than a threshold, they wll not be assocated and the relatve element n matrx D wll be set to nfntude. Based on analyzng the matrx D, a correspondence matrx C at tme s constructed to assgn the foreground regon measure to the trac. The followng s the detals of constructon. 1. Frstly, all the elements of matrx C are set to zero. 2. Fnd the poston of the mnmal elements n every row α = { α 1, K, α m } and column β = { β 1, K, β n } of D through the followng euatons: D (, α ) = mn( D (, )), = 1, L, n (6) D ( β, ) = mn( D (, )), = 1, L, m 3. Fnally, add one to the correspond element n matrxc. C (, α ) = C (, α ) + 1, = 1,..., m (7) C ( β, ) = C ( β, ) + 1, = 1,..., n (8) Three possble values may found n the element of matrx C : Zero, one and two. Zero means no selecton. One represents one selecton happens. Two means the trac and the measure selects each other both. Fve possble results can arse n the matrxc : A trac s not assocated to any measure (All the elements n a row are zero). A measure s not assocated to any trac (All the elements n a column are zero). A trac s assocated to more than one measure (More than one element n a row are larger than zero). A measure s assocated to more than one trac (More than one element n a row are larger than zero). A measure s assocated to a trac (The element value s two). In ths paper, f an element value n matrxc euals to two, the measure wll assgn to the trac, and all the elements n the same row and column of the dstance matrx D are updated to nfntude. After that, a new correspond matrx C s constructed from the updated dstance matrx D. Ths process wll eep on loopng untl none of the elements value of matrx C euals to two. Fnally, the foreground measures and exstng tracs are classfed nto three parts: Non-matched trac, nonmatched measure, matched trac and measure. The above assocaton method assgns one measure to one trac and can not handle mergng and splttng event, n whch one measure may assgn to multple tracs and one trac may assgn to multple measures. To solve ths problem, we develop a mergng and splttng detecton procedure based on the obtaned classfcaton results. For those non-matched trac, a mergng detecton algorthm s used to decde whether the trac s merged by another measure or s mssed. If a mergng happens, a new group s generated. If the trac s mssed, the confdence of the trac wll be decreased, once t drops below a specfc threshold, the trac wll be deleted. For those non-matched measures, a splttng detecton module s developed to decde whether the measure s splt from an actve trac or t s a new target. When a splttng event s confrmed, a feature correspondence module (see secton 4) s performed to labelng each obect correctly.

5 Mergng mght occur due to a non-matched trac overlapped wth a measure. Ths udgment s based on the assumpton that there must be overlapped area between the ntal mergng boundng box and the merged obect (Fgure 3, frst row).ths s a vald assumpton when the segmentaton process s fast enough, as soon as obect touches wth each other at tme +1, a large boundng box contans all the merged obects wll be created and t has large overlappng areas wth the merged obects at tme. Fortunately the movng obect segmentaton method mentoned n secton 2 acheves 20fps n the survellance system, fast enough to detect mergng event even wth hgh speed obects. Smlar to the mergng method above, splttng s detected due to a non-matched measure overlapped wth a trac (Fgure 3, second row). When a group splts, each splt obect wll be labeled correctly wth a feature correspondence method (See secton 4). A Fgure 3. A scenaro of blob mergng and splttng detecton. The frst row contans the blob mergng events and the overlappng areas. The second row contans the splttng events. 4. Feature Correspondence B When a mergng event has been detected, the nformaton of the occluded trac wll be added nto the group, for nstance ts traectory and certan feature. After that, the entre group wll be traced as one target. When t splts, the feature nformaton of the occluded obect n the group wll be used for correspondence. An mportant pont s how to select the sutable feature. To reduce the complexty of the tracng system, we use 2D feature of the obect. Durng the last two decades, three classes of features have been wdely consdered for tracng purpose: moton, appearance and color. The moton feature based methods smooth the poston and moton of the obect only, the obect has to be detected by an ndependent technue. Once occluson happens, the measure of the flter can not be acured and the confdence of tracng result s decded by the occluded obect s moton character and the maxmal duraton of the occluson, and t wll be decreased accordng to obect s non-lnear moton and long tme occluson. Appearance B A B +1 A B A A B +1 B A model has got much attenton recently [10,11,13] and t s powerful n dealng wth short tme partal occluson. However the obect appearance can change a lot after long tme occluson, moreover, t can not handle complete occluson whch s ute often n complex real world survellance. Consderng the complex real world survellance scene, n our system, we use color feature to measure smlarty. Let O ( ) = { O ( ), O ( ),..., O ( )} 1 2 u denotes the occluded obects of group and S ) = { S ( ), S ( ),..., S ( )} denotes the splt obects ( 1 2 v from the group at tme. PO denotes the color dstrbuton of the th occluded obect O () and represents the color dstrbuton of the th splt PS obect S () of group. Snce n the experments, we acheved the same ualtatve correspondence results wth RGB and HSV color space, we chose the RGB space and computng the color dstrbuton wth Nr Ng Nb bns. In addton, nstead of computng the color hstogram of the boundng box, we use a strategy of combng moton segmentaton to provde more accurate color feature. The color dstance matrx D (rows correspond to occluded C obects and columns to splt foreground regon) s measured usng the Kullbac-Lebler (KL) dstance (9) between the two color dstrbutons and use the assocaton algorthm mentoned n secton 3 to assgn each splt obect. Nr Ng Nb DC, ) = l= 1 ( PO ( l) log( PO ( l) / PS ( l)) (9) where = 1, K, u, = 1, K, v. Consderng the number of splt targets s less than the total number of the occluded obects n the group, after the correspondence step, the splt foreground regons and exstng occluded obects are classfed nto two parts: Non-matched occluded obect, matched obect and measure. If the number of non-matched occluded obect euals to zero, the group wll be termnated. 5. xpermental Results The system s mplemented on standard PC hardware (Pentum IV at 3.0GHz) and wors at 15-20fps. The vdeo mage sze s 320x240 (24 bts per pxel) captured by Sony DCR9 at 25fps. The system s tested n typcal ndoor and outdoor envronments for handlng ghost stuaton, bacground modelng and occluson. In the system we use color hstogram n RGB color space wth 10x10x10 bns for feature correspondence. We delberately selected clps taen under dffcult condtons,

6 especally those wth bacground changng and occlusons. The followng presents results Tracng Multple Interactng People Indoor Fgure 4 shows an example of tracng two nteractng persons n an ndoor envronment (see vdeoclp1.av of the supplementary materals). The red and blue box shows the poston of the person wthout occluson. Green box shows the poston of the group n whch people are occluded wth each other. Ther traectores are shown wth red and blue lnes. In ths seuence, the target No.2 (Target ID s labelng at the top left corner of the boundng box) changes hs moton drecton suddenly (Fgure 4, frame #974, red lne), the moton model based tracng approaches are always fal at ths stuaton. At frame #1176, occluson happens agan and after that the two persons are traced as a whole group untl frame #1665. Durng ths process, the target No.1 s completed occluded by target No.2 (Fgure 4, frame #1562) for several frames and the occluson lasts for 183 frames (From frame #1482 to frame #1665). It s hard for those template matchng methods or appearance models to handle ths stuaton. Once the end of occluson has been determned (Fgure 4, frame #1665), the people can be recaptured and correct labeled Tracng Multple Obects Outdoor Fgure 5 gves an example of tracng multple obects wth ghost and occluson n outdoor scene (see vdeoclp2.av of the supplementary materals). The mage on the left of a par measures reactvty of the bacground reflects changes from two vehcles (Target No.161 and Target No.168) that start ther motons after havng prevously been part of the bacground. The rght mage n each frame shows the traectores of multple obects. Green box dsplays the poston of the group n whch obects are occluded wth each other. Tae target No.161 as example, when t starts moton, a ghost labeled as No.166 s left (Fgure 5, frame#171). Several frames later, the correct bacground can be updated (Fgure 5, frame #407) and the correct segmentaton can be acheved. It s hard for mxture Gaussan bacground model to get the same fast and accurate update result. In addton, the system successful handles occluson of target No.143 and target No.168 (Fgure 5, frame #407, frame #421). We report results of the system on the most recent dataset of C Funded CAVIAR proect [23] and PTS 2001 dataset n Fgure 6. In the CAVIAR dataset (Fgure 6, frst row, see vdeoclp3.av of the supplementary materals ), two people meet, fght and run away. Heavy occluson happens durng the fghtng (Fgure 6, frst row, frame #150, frame #194) and the system correctly tracs each person before, durng and after fghtng. Two occlusons happen n PTS seuence (Fgure 6, second row, frame #996, frame #1078) and obects are traced correctly before and after occlusons. Images n Fgure 7, selected from the real tme survellance system, represent typcal scenes n a resdental area, ncludng traffc road survellance, entrance of parng lot, bac yard and entrance of the buldng. 6. Concluson A real-tme multple obect tracng system s presented. xperments on complex ndoor and outdoor envronments show that the system can deal wth dffcult stuatons such as ghosts and bacground changes. Moreover, t can trac multple obects wth long-duraton and complete occluson. Whle the system s hghly computatonally cost effectve and accurate, future wor ncludes developng a real tme hgh-level events understandng system. References [1]A. Amer,. Dubos, and A. Mtche, Real-tme system for hgh-level vdeo representaton: applcaton to vdeo survellance, n Proc. SPI Int. Symposum on lectronc Imagng, Conf. on Vsual Communcaton and Image Processng (VCIP), Santa Clara, USA, vol. 5022, pages , Jan [2]Collns et al. A System for Vdeo Survellance and Montorng.VSAM Fnal Report, Techncal report CMU-RI-TR , Carnege Mellon Unversty, May, [3]Fengun Lv, Jnman Kang, Ram Nevata, Isaac Cohen, and Gérard Medon, Automatc Tracng and Labelng of Human Actvtes n a Vdeo Seuence, Proceedngs of the 6th I Internatonal Worshop on Performance valuaton of Tracng and Survellance (PTS04), Prague, Czech Republc, May, [4]C.R. Wren, A. Azarbayean et al, Pfnder: Real Tme Tracng of the Human Body. I Trans. Pattern Analyss and Machne Intellgence, vol. 19, no. 7, July [5]C. Stauffer and W. Grmson, Learnng Patterns of Actvty Usng Real Tme Tracng. I Trans. Pattern Analyss and Machne Intellgence, vol. 22, no. 8, pages , Aug [6]Docstader et al, Multple camera tracng of nteractng and occluded human moton, Proceedngs of the I, Vol: 89, Issue: 10, pages , Oct [7]Tng-Hsun Chang, Shaogang Gong, and ng-jong, Tracng multple people under occluson usng multple cameras. In Proc.11th Brtsh Machne Vson Conference, [8]S.L. Docstader and A.M. Tealp, Multple camera fuson for mult-obect tracng. In Proc. I Worshop on Mult-Obect Tracng,pages ,2001. [9]R.Cucchara, C.Grana, G.Tardn, R.Vezzan, Probablstc people tracng for occluson handlng, Proceedngs of the 17th Internatonal Conference on ICPR 2004, Vol:1,pages Aug , [10]How-Lung ng, et al. A bayesan framewor for robust

7 #703 #737 #974 #1176 #1383 #1482 #1562 #1665 Fgure 4. A seuence of two nteractng people tracng under heavy occluson n an ndoor envronment. The red and blue box show the poston of the person wthout occluson. Green box shows the poston of the group n whch people are occluded wth each other. The red and blue lne dsplays the traectory of the two persons. Note that long-duraton complete occluson (Frame #1562) s correctly handled. #80 #171 #364 #407 #421 #471 Fgure 5. A seuence of multple obect tracng wth ghost and occluson. The left mage n each frame shows the reactvty of bacground model when ghost happens. The rght mage n each frame shows the traectores of multple obects. Green box dsplays the poston of the group n whch obects are occluded wth each other. Other varous color lnes dsplay the traectores of the obects. Note that two ghosts (cased by Target No.161 and Target No.168) and one occluson (Frame #407) exst n ths seuence. After several frames of the ghost happen, the correct bacground can be updated and the correct segmentaton can be acheved.

8 #129 #150 #194 #222 #996 #1058 #1078 #1208 Fgure 6. Tracng results of CAVIAR and PTS 2001 seuence. The frst row contans tracng result of two people meet, fght and run away, the data s come from the CAVIAR proect. The second row contans tracng result about PTS2001 dataset. The system operates on AVI vdeo fle generated form the datasets above for mage sze of 320x240. The system wors at 15-20fps. a b c d Fgure 7. Real tme multple obect detecton and tracng results n real survellance scene. These seuences are selected n the real tme survellance system and represent the typcal scenes n a resdental area. a) Road survellance. b) ntrance of parng lot. c) Bac yard. d) ntrance of the buldng. human detecton and occluson handlng usng human shape model. Proceedngs of the 17th Internatonal Conference on ICPR 2004, Vol: 2, pages , August 23-26, [11]Yng Wu, Tng Yu, Gang Hua, Tracng appearances wth occlusons, Proceedngs of I Computer Socety Conference on Computer Vson and Pattern Recognton, Vol: 1, pages , June [12]A. Senor, et al. Appearance Models for Occluson Handlng. Proc. 2nd I Int. Worshop on PTS, Kaua,Hawa,USA, December 9,2001. [13]A. Senor, Tracng wth Probablstc Appearance Models, Proc. CCV worshop on Performance valuaton of Tracng and Survellance Systems, pages 48-55, 1 June [14]N. T. Sebel, S. Mayban, Fuson of Multple Tracng Algorthms for Robust People Tracng, 7 th uropean Conf. on Computer Vson, Denmar, Vol.IV, pages , May [15]Heu T. Nguyen and Arnold W.M. Smeulders, Fast Occluded Obect Tracng by a Robust Appearance Flter, I Transactons on Pattern Analyss and Machne Intellgence. Vol.26, No.8, pages , August [16]H. Tao, H. S. Sawhney, and R. Kumar, Dynamc Layer Representaton wth Applcatons to Tracng. Proc.Computer Vson and Pattern Recognton, Vol.2, pages ,2000. [17]A. Ylmaz and M. Shah, Contour-Based Obect Tracng wth Occluson Handlng n Vdeo Acured Usng Moble Cameras. I Transactons on Pattern Analyss and Machne Intellgence, Vol. 26, No. 11, November, [18]M. Isard and A. Blae, CONDNSATION Condtonal Densty Propagaton for Vsual Tracng, Internatonal Journal on Computer Vson 1(29),1998. [19]M. Isard and A. Blae, Contour Tracng by Stochastc Propagaton of Condtonal Densty, In CCV '96, pages ,1996. [20]A. Doucet, N. Fretas, N. Gordon, Seuental Monte Carlo Methods n Practce, Sprnger [21]R. Rosales and S. Sclaroff, Improved Tracng of Multple Humans wth Traectory Predcton and Occluson Modelng, Proc.I Conf.on Computer Vson and Pattern Recognton. Worshop on the Interpretaton of Vsual Moton, Santa Barbara,CA,1998. [22]P. KawTraKulPong, R. Bowden, An mproved adaptve bacground mxture model for real-tme tracng wth shadow detecton, In Second uropean Worshop on Advanced Vdeobased Survellance Systems,2001. [23]CAVIAR vdeo seuences. rbf/caviar/