Track-based and object-based occlusion for people tracking refinement in indoor surveillance

Transcription

1 Trac-based and objec-based occlusion for people racing refinemen in indoor surveillance R. Cucchiara, C. Grana, G. Tardini Diparimeno di Ingegneria Informaica - Universiy of Modena and Reggio Emilia Via Vignolese, 905/b Modena, Ialy {cucchiara.ria,grana.cosanino,ardini.giovanni}@unimore.i ABSTRACT People racing deals wih problems of shape changes, selfocclusions and rac occlusions due o oher inerfering racs and fixed objecs ha hide pars of he people shape. These problems are more criical in indoor surveillance and in paricular in home auomaion seings, in which he need o merge informaion obained form differen cameras disribued around he house calls for he inegraion of reliable daa obained during ime. Therefore, racing algorihms should be carefully uned o cope wih occlusions and shape changes, woring no only a pixel level bu also a region level. In his wor we provide a novel echnique for objec racing, based on probabilisic mass and appearance models. Occlusions due o oher racs or due o bacground objecs and false occlusions are discriminaed. The classificaion of occluded regions of he rac is exploied in a selecive model updae. The racing sysem is general enough o be applied wih any moion segmenaion module, i can rac people ineracing each oher and i mainains he pixel o rac assignmen even wih large occlusions. A he same ime, he model updae is very reacive, so as o cope wih sudden body moion and silhouee s shape changes. Due o is robusness, i has been used in differen experimens of people behavior conrol in indoor siuaions. Caegories and Subjec Descripors I.4.8 [Image Processing and Compuer Vision]: Scene Analysis moion, racing. General Terms Algorihms, Performance, Design, Experimenaion, Theory. Keywords People racing, Video surveillance, Occlusions, Probabilisic models. 1. INTRODUCTION Tracing is one of he mos criical sep in processes of people moion capure, people behavior conrol and indoor video surveillance. The racing module should be very efficien, in order no o affec he speed of he whole process and, a he same ime, i should be very reacive, o adjus he model o sudden changes of silhouee s shape and very robus o occlusions due o oher people or objecs presen in he environmen. A ypical scenario is indoor people behavior conrol: for example, a home or office could be insrumened wih a large number of video sensors, ha, woring ogeher, can idenify people wihin he home and acively rac hem as hey move hroughou he environmen, providing services ha mae life easier such as auomaic lighing, naural human-home inerfaces, and surveillance for securiy. Bu o his ind of ineracion he coordinaion sysem should be provided wih he mos informaion possible, in order o reduce he difficuly of he idenificaion as, and o supply a coninuous nowledge of he differen raced people posiions. In ypical applicaions no more han a single fixed camera for each room can be considered, o limi he overall insallaion coss. Therefore, he sysem should supply high level informaion, such as people color appearance and shape, no only for people racing from a single poin of view, bu also o handle he camera hand off and he coordinaion beween differen cameras. In his framewor, people racing mus cope wih problems of frequen shape changes, self occlusions, and oher ypes of occlusions caused by moving objecs (rac-based occlusions), or fixed objecs included in he bacground model (objec-based occlusions). Therefore racing canno be provided a pixel level only, predicing he pixel moion, bu mus be suppored by assumpions a objec-level, assuring spaial coherency of poins of he same shape during he ime. Accordingly, we address he problem of racing by exploiing appearance and probabilisic models, suiably modified in order o ae ino accoun he shape variaions and he possible region of occlusion. The appearance image of a rac represens he nowledge we have of an objec during racing. For each poin of he rac, AI ( x ) is he esimaed aspec of he objec, described in he RGB space (see Figure 2.b). The corresponden probabiliy mas PM ( x ) defines he probabiliy ha he poin belongs o he rac (see Figure 2.c). Since AI and P M are defined a poin level, hey are a represenaion of he emporal coherency of he poin, giving us he informaion of how much

2 he poin is a inlier since i has been deeced and assigned o he rac during he ime. Many wors use AI ( x ) and P ( ) M x, updaing hem frame by frame wih adapive funcions depending on he single poin only. Conversely, we propose a model ha is based on AI ( x ) and PM ( x ) o provide racing, bu explois spaial informaion in he updae process. We verify spaial coherency of he racs, depending on he ype of occlusion, a a shape level by means of global measures (namely Confidence and Lielihood) and a a region level, analyzing no visible regions of he rac. 2. RELATED WORKS Two aspecs are imporan in he analysis of racing echniques: he nowledge represenaion and he models of emporal correlaion. For he firs poin, in lieraure objec-based and image-based approaches have been proposed. Objec-based approaches use a represenaion of he rac wih a binary mas, exraced by segmening he image, and a se of shape descripors lie silhouees or corners, as in [2] in which a emporal graph is used o produce a dynamic emplae ha describes he average shape of he objec. Image-based approaches use in addiion feaures exraced also from he aspec of he objec in he image iself, as color hisograms [12] or mixures of Gaussians [10]. These can han be clusered o verify spaial relaions as in [13] in which similar hisogram s bins are merged o produce spaial coheren areas or in [16] in which he mixure of Gaussians describing he bacground allows also for spaial clusering based on he esimaed mean and variance. A se of wors uses boh objec and image-based paradigms, exploiing a probabilisic descripion of he presence of a pixel in he objec, along wih color hisory images [6,17,14]. For wha concerns emporal correlaion, mos of he wors employ he Kalman filer [11,17], bu also Mone Carlo approaches as he Condensaion algorihm [7], and even simpler firs order approaches as in [14,6]. In lieraure, many wors address people racing wih occlusion handling, bu only few of hem manage he pixel assignmen during he occlusion, in order o eep he nowledge of he rac while he occlusion occurs. The wors [8] and [10] solve he problem of occlusions beween racs. In [8] classes of similar color defined wih EM algorihm are defined o segmen people, raced frame by frame wih a maximum a poseriori probabiliy approach. In [10] pixels assignmen is guided by color hisograms ha model he a priori probabiliy and again a Bayes rule is used o form he poserior probabiliy: hus a visibiliy index is buil o provide informaion on he deph ordering of racs. The auhors of [1] exploi a sereo vision sysem o deal wih he occlusions and o correcly segmen each person in he scene. Furhermore, similar o ohers [9,14], hey use a mas and an appearance emplae for each rac o resolve he emporal racing. In [15] he racing sysem is realized wih he fusion of hree cooperaing pars: an Acive Shape Tracer, a Region Tracer and a Head Deecor. The Region Tracer explois he oher wo modules o solve occlusions. 3. TRACKS, VISUAL OBJECTS AND MACRO OBJECTS The racing we propose is oally independen from previous seps of objec segmenaion. Given he acquisiion from a single fixed camera, le us assume o have, for each frame, a se V of Visual Objecs: { 1,, }, V = VO K VOn VOj = { BBj, M j, Ij, cj}. Each Visual Objec VO j is a se of conneced poins deeced as moving by he segmenaion algorihm and described wih a se of feaures: he bounding box BB j, he blob mas M j, he Visual Objec s color emplae I j and he cenroid c j. During he racing execuion, we compue a se of racs τ a each frame, ha represens he nowledge of he objecs presen in he scene: τ = { T1.. T } m wih T = { BB, AI, PM, PNO, c, e }, where BB is he bounding box; AI is he Appearance Image, i.e. he esimaed aspec (in RGB space) of he rac poins: each value AI ( x ) represens he memory of he objec s poin previously raced; PM is he probabiliy mas: each value of PM ( x ) defines he probabiliy ha he poin x belongs o he rac T ; PNO is he probabiliy of non occlusion associaed wih he whole rac, ha is he probabiliy ha he rac is no occluded by oher racs; e is he moion vecor esimaed for he nex frame. Hereinafer, in order o manage a poin eiher of he VO or of he Trac, we will wrie improperly x VO or x T, meaning ha x BB and eiher he VO s mas M or he probabiliy mas PM of T in he poin x is no zero. In order o inegrae in a single srucure all he possible condiions of objecs ineracion (merging, spliing, overlapping), he process sars wih he consrucion of a 1 Boolean correspondence marix C beween he V and T ses. The elemen C, jis se o one if he VO j can be associaed o he 1 rac T. The associaion is esablished if he rac (shifed ino is esimaed posiion by means of he vecor e ) and he VO can be roughly overlapped, or, in oher words, if hey have a small disance. I is compued as a Bounding Box Disance (BBd) as in he following equaion: (a) (b) (c) Figure 1. People rac example. (a) Visual Objec and is rajecory, (b) Appearance Image, (c) Probabilisic Mas

3 RGB appearance of he single poin and he resuls of he rac evaluaion phase. ( ) ( j, T) = min min ( j, +, +, j ) BBd VO (a) (b) (c) (d) Figure 2. Deph order pixel assignmen. x BB y j BB j c x e c e y. (1) In he marix C five differen cases can arise: 1) a rac is no associaed o any VO: he rac is missed; 2) a VO is no associaed o any T: a new objec is enered ino he scene and a new rac is generaed; 3) a T is associaed o more han one VO; 4) many racs are associaed o he same VO; 5) many racs are associaed o many VOs. In he las hree cases, he racing sysem has o cope wih problems of rac spli, rac merge or rac overlap. This wor is specially oriened o solve hese las cases, very frequen in indoor environmens wih ineracions beween differen people and beween people and objecs. To his aim, we define he concep of Macro-Objec (MO) as he union of he VOs associaed o he same racs. Iniially a MO is creaed for each VO, hen couples of MOs ha have a leas a rac in common are merged. This sep is ieraed unil each rac is associaed o a single MO only. Thus, hereinafer, he racing will wor independenly on each single MO and on he subse % τ τ associaed wih ha MO. By adoping MOs insead of he segmened VOs in he racing module we can ge rid of he problem of managing he many-omany correspondence case. In general, in fac, a single segmened VO, generaed from overlapped people, has poins ha should be assigned o differen racs, or some disjoin VOs (due o segmenaion errors) should be associaed o he same rac. 4. PEOPLE TRACKING The racing ieraes he designed algorihm a each frame and for each pair ( MO, τ% ). A each ieraion, for each rac Ti % τ, he algorihm is composed by hree seps: 1) rac alignmen and pixel o rac assignmen: he sysem searches for he bes pixel-level alignmen beween T i and MO, and assigns each pixel of he MO o he rac wih he highes probabiliy o have generaed i; 2) rac evaluaion: wo measures (Confidence and Lielihood) of T i are evaluaed and he pars of he racs ha are no visible in ha frame are segmened (we call hem non-visible regions) and classified as poenial occluded regions; 3) rac updae: he nowledge of he rac is updaed according wih an adapive model ha aes ino accoun he acual A final rac se refinemen process evaluaes he se of generaed racs, in order o decide if i is useful o merge or spli racs. The merging/spliing problem is very criical in indoor environmen since shadows, segmenaion errors, and large occlusions, occurring when a person is enering in he observed scene, generae separae VOs ha can erroneously creae separae racs ha mus be merged. On he conrary, if a group of people eners he scene a once, a furher analysis on he rac appearance is needed, when he people will wal in differen direcions. Because of his, some furher high level consideraions, based on he moion and rajecory coherence, are employed o deec his siuaions and consequenly merge and spli he corresponding racs. 5. TRACK ALIGNMENT In his phase he esimaed posiion of each rac is refined wih he displacemen δ = ( δx, δ y ) ha maximizes a fiing funcion P FIT. PAPP( I( x-δ), AI ) PM P (, ) MO FIT T δ = x PM x T where APP ( i, j ) P RGB RGB measures he correspondence beween he acual RGB color of he poin in MO and he appearance model of he rac. As in [14], we use a spherical Gaussian o approximae he pixel disribuion around he mean µ sored by he model (2) 2 RGBi-µ i P 2 2 APP (, ) (2 πσ ) RGB = e σ i µ i (3) Here we supposed ha he R,G,B variables are uncorrelaed and wih idenical variance σ 2. This is ieraed for all he racs associaed wih a MO, wih a order proporional o heir probabiliy of no occlusion. The δ displacemen is iniialized wih he value e and searched wih a gradien descen approach. The alignmen is given by δbf ( T ) = arg max ( PFIT ( T, δ )). Afer each fiing compuaion, he poins of he δ MO maching a rac poin wih high P APP are removed and no considered for he following racs fiing. Figure 2 shows a single MO (wo overlapped people) corresponding o wo racs: Figure 2.a is he image, Figure 2.b is he segmened MO afer merging of VOs, and Figure 2.c shows he MO s poins remained afer he assignmen of he fron mos rac, on which he rearmos rac is fied; Figure 2.d is he probabiliy mas of his rac. 6. PIXEL TO TRACK ASSIGNMENT All poins of MO mus be assigned o a rac. If a MO is in correspondence wih a single rac, he assignmen is sraighforward. Insead, in presence of rac occlusions, when

4 1 1 Lielihood 0,8 0,6 0, ,2 0 wo or more racs conend poins of he same MO, we exploi a poseriori probabiliy o solve he assignmen: ( x) PT ( x ) ( ) P T P T = P. (4) The condiional probabiliy is he produc of wo erms: P( xt ) = PAPP( I( x-δbf), AI ) PM ( x ). I aes ino accoun he difference beween he colors of he acual pixel and of he rac appearance (as in Equaion 3), weighed by he probabiliy ha he poin belongs o he rac PM ( x ). In order o cope wih rac-based occlusions, he PT ( ) is suiably modeled as he a priori probabiliy of seeing T, defined as a probabiliy of no occlusion (see secion 8 for deails). Each poin will be assigned o he rac ha maximizes PT ( x) and he se of poins assigned o he rac T is named A. 7. TRACK EVALUATION To evaluae rac and occlusion characerisics, he spaial informaion a wo levels is exploied: a objec-level, a value of reliabiliy of he fi measure is compued from he fiing value P FIT, a region-level, possible occlusions are classified. 7.1 Lielihood and Confidence To cope wih large occlusions we refined he model by rewriing Equaion 2 as: Frame Frame (a) (b) Figure 3. Values of Lielihood (a) and Confidence (b) for he sequence in Figure 1. Confidence 0,8 0,6 0,4 0, PFIT ( T, δbf ) = Lielihood Confidence = PAPP( I( x-δbf), AI ) PM PM (5) x MO x MO PM PM x MO x T The firs erm is a measure of how similar are he corresponding pixels of he MO and of he rac; he second erm is he percenage of rac poins, weighed wih heir probabiliy, ha are visible on he curren frame and belonging o he MO. Accordingly, when he produc of Lielihood and Confidence is low, he rac is considered oally occluded (and since r δ BF is no reliable, he esimaed e is used as he mos reliable displacemen). Insead, immediaely afer an occlusion we wan o reac wihou waiing for he bes fi value o reurn o higher values: herefore, if he previous produc is low, bu he Confidence value is growing wih respec o he previous frame, he esimaed posiion is updaed anyway. Figure 3 shows he variaion of Lielihood and Confidence for he wo racs in sequence of Figure 2. As he rearmos rac become occluded, is confidence value decreases, while he lielihood has only lile changes due o shape and color variaions, so he displacemen is compued using he esimaion given by e only. The lowes poin in Confidence value represens he momen of maximum occlusion. Afer frame 102, he rearmos rac has a high Lielihood (0.76) and a low, bu growing, Confidence (0.32) and hus we accep he posiion refinemen given by r δbf (a) (b) (c) (d) Figure 4. Example of erroneous rac freezing. (a) Original image; (b) an occlusion causes he Confidence value o go very low; (c) he rac is sill mainained; (d) he rac is los. 7.2 Occlusion classificaion Due o occlusions or shape changes, some poins of he racs remain wihou any correspondence wih a MO poin. Oher proposed echniques ha exploi probabilisic appearance models wihou coping wih occlusions explicily, use only he se of assigned poins ( A ) o guide he updae process [14]; he mas probabiliy a each poin x { A } is reinforced, while a each poin x { T A} decreases. In our wor, he adapive updae funcion is enriched by he nowledge of occlusion regions. When his siuaion happens, he

5 T A NV Area Filer MinArea (a) (b) (c) Fig. 5. Loss of par of he model because of an occlusion. (a) Inpu frame; (b) Curren segmenaion; (c) Appearance model and Probabiliy mas. Confidence value associaed o he rac goes lower, because a par of i is no more visible. In order no o lose he memory of he objec appearance, he simples soluion could be he use of a hreshold on he Confidence value o freeze he rac updae. In his approach wo possible problems may be encounered: if he value is decreasing bu i is sill higher han he hreshold he model is sill updaed and he probabiliy values in correspondence wih he occluded region begin o decrease; if he occlusion is quie long, hey disappear. In his case we have an informaion loss (See he problem in Figure 5.c where he appearance of legs is los). If he value goes under he hreshold he model is no updaed anymore. In his way, he hidden par is perfecly remembered, bu any change in he rac appearance is no aen ino accoun. A problem arises when, while is freezed, i changes he visible par appearance as, for insance, when a person changes is direcion, abruply. In Figure 4, i is possible o see a person ha roaes on his axis and bends on a side. In his case, since he occlusion area is quie exended, he Confidence value is low, so he model canno adap o he shape variaions; he momen ha i changes is direcion (Figure 4.c), he rac is los again. We can deduc ha he sysem wors correcly only in cases in which he occlusion lass for a shor ime, enough no o lose zones of he rac, and if subsanial changes in he objec appearance do no happen. The choice of rac freezing has some difficulies in all he siuaions in which sudden variaions of he rac appear. Given hese consideraions, he inroducion of an higher level reasoning is necessary in order o discriminae beween occlusions and oher shape changes. The se of non visible poins NV = { T A } are he candidae poins for occlusion regions: in general, hey are he poins of he racs ha are no visible anymore a he frame. Afer a labeling sep, a se of no visible regions (of conneced poins of NV ) is creaed, neglecing sparse poins or oo small regions. Non visible regions can be classified in hree classes: 1) rac-based occlusions R TO : due o overlap of anoher rac, closer o he camera; herefore he pixels of his region were assigned o he oher rac; 2) bacground objec-based occlusions R BOO : due o (sill) objecs, posiioned ahead of he rac; Border Exracion Border Analysis RBOO COR i BCOR i RAO BKG EDGE %EdgeTh Figure 6. Occlusion region classificaion algorihm. 3) apparen occlusions R AO : regions no visible because of shape changes, silhouee s moion, or self-occlusions. The presence of occlusions can be deeced wih he Confidence value of Equaion 5 decreasing above an alering value, since in case of occlusion he rac s shape changes considerably. In poins of acual occlusions (classes 1 and 2), heir rac model should no be updaed since we do no wan o lose he memory of he people appearance. Insead, if he Confidence decreases due o a sudden shape moion (apparen occlusion), no updaing he rac would creae an error. The soluion is a selecive updae according o he region classificaion. The R TO regions have already been disinguished in he assignmen phase: hey are composed by he poins shared beween rac T and oher racs T i bu no assigned o T. In order o disinguish beween cases 1) and 2) he bacground objecs should be nown. Even if we do no have an exac 3D model for each objec in he scene, mos of he segmenaion algorihms from fixed camera mae a bacground model available. I can be compued a each frame in bacground suppression segmenaion echniques, or can be esimaed only when needed. An approximaed echnique based on an edge analysis of his bacground image is proposed. The algorihm is depiced in Figure 6: from he whole se of no visible poins, we only eep hose wih a no negligible value of he probabiliy mas in order o ge rid of he noise due o moion. The remaining se of poins is segmened ino conneced regions. Then, for each region, he area weighed wih he probabiliy values is calculaed, and oo small regions are pruned ou (MinArea parameer in Figure 6). The remaining regions are he Candidae Occlusion Regions ( COR i ), ha mus be discriminaed ino bacground objec occlusions and apparen occlusions. The borders of he COR i are exraced, and called BCOR i. A he same ime, he edges of he bacground model are made available by a simple Sobel edge deecor. The pixels of BCOR i corresponding o edge pixels of he bacground are classified as bounding pixels, while he ohers are said no bounding pixels. If

6 Table 1. Sysem performances. Video #pe #fr #C FP FN V V V V PETS 2002 TR PETS 2002 TR PETS 2002 TE PETS 2002 TE he percenage of bounding pixels is sufficienly high (ypically 40% of he region borders), we can infer ha an objec is hiding a par of he rac, and he region is labeled as R BOO, oherwise as R AO. In Figure 7 an example is shown: a par of a person is occluded; analyzing he border of he candidae occlusion region (Figure 7.c), we find ha he majoriy of bounding pixels is locaed in correspondence of bacground edges. Thus, his no visible region is classified as R BOO and he corresponden probabilisic and appearance model is frozen, ha is neiher reinforced neiher weaened (he dar par in Figure 7.d). 8. SELECTIVE TRACK UPDATE As he final sep, he probabiliy mas, he appearance mas and he probabiliy of no occlusion are updaed wih adapive funcions. In paricular, x T 1 ( 1 λ) x AK ( ) ( TO ) ( BBO ) 1 oherwise λpm + 1 PM = PM x x R x R λpm AI 1( ) ( 1 λ) ( ) λai x + I x x AK = 1 AI oherwise When he rac is generaed PM ( x ) is iniialized o an inermediae value (0.4 when λ=0.9) while he appearance image is iniialized o he image I ( x ). Defining Po i as he probabiliy ha rac Ti occludes T, he non-occlusion probabiliy, PT ( ) PNO( T) used in he Bayes rule of Equaion 4, is compued as a value proporional o he number ai of shared poins assigned o T i and no o T. In paricular: (6) (7) PNO ( T ) = 1 max ( Po i ), (8) i= 1.. m Figure 8. Correc rac-based occlusion resoluion. (a) (b) (c) (d) Figure 7. Edge pixel classificaion and selecive updae. (a) Bacground edges, (b) rac, (c) border of non visible region, (d) appearance image and probabilisic mas. a calling i a β + i i =, A Po i updae model is: i 0 βi < ϑoccl 1 Po (1 ) i 0 i βi Po a = = i. (9) a i ai 0 1 a (1 β ) i i Po i βi e + Finally, he moion vecor e is esimaed according o a consan speed assumpion, bu enforced by a segmened rajecory schema. Saring from a reference iniial posiion, a cerain number of successive moion vecors are linearly inerpolaed by finding he leas squares soluion. The soluion vecor is he moion esimaion. In order o chec if he inerpolaion describes correcly he las vecors in he observaion window, we evaluae he raio beween he wo eigenvalues of he principal direcion compuaion and also if he angle or modulus has changed much from he firs value. If he soluion fails hese checs, a new reference posiion is creaed and a new direcion can be searched. In his way, an adapive finie window is used o infer he fuure moion of he objec, able o cope wih change of direcion in a robus way. This echnique has he advanage of being able o handle also non lineariies in he measured moion, wih respec o classic esimaion echniques. 9. RESULTS AND CONCLUSIONS The sysem has been devised for a projec of Indoor Surveillance o conrol he people behavior in he house and deec dangerous siuaions, as people falling and lying on he floor moionless for a long ime. The iniial descripion of he video surveillance sysem was described in [3]. In [4] a reliable people posure classificaion echnique is presened. To cope wih a precise frame by frame people behavior conrol, a complee racing module wih occlusion handling capabiliies was needed. This complex bu complee process has been esed over days of indoor video surveillance in wo rooms equipped wih fixed camera, wih some acors and indoor furniure. Moreover, i has been esed over he videos of PETS 2002, in which people wal and inerac behind a shop window. Figures 2 and 4 are examples of frames of videos V2 and V3 respecively. Figure 8 shows how he rac based occlusion in V1 are correcly managed. Table 1 shows he performance of he sysem over eigh sequences. The values in column #pe is he number of people presen in he scene, #fr is he number of frames, #C is he number of correc

7 assignmens a rac level and FP and FN are he number of false posiives and false negaives respecively measured agains a manual ground-ruh. The former are cases in which wo or more racs are assigned o he same person, while he laer are he number of imes in which no racs are assigned o a person. In he video TR3 he high error rae in FP is due o he fac wo people ener ogeher in he scene and he sysem has no he possibiliy o see hem as separae objecs. Imporan experimens are V3 and V4 experimens, where large occlusions due o furniure and rac overlaps occur: in hese videos a percenage of abou 88% of correc assignmen is reached. The racing approach is no oo compuaionally inensive. In our experimen, he indoor video surveillance is able o process abou fifeen frames per second on a sandard PC including an iniial visual objec segmenaion module wih bacground suppression, he shadow removal module [5] and a furher people posure classificaion process [4]. The edge-based mehod is able, on average, o correcly classify he 85% of non visible regions. This approach could be furher refined bu i is enough precise o allow a good reaciviy o silhouee s shape change and, a he same ime, a good memory of he appearance model also when a person remains occluded by saic objecs for a long ime. Therefore he proposed racing module is a general scheme ha explois probabilisic funcion and appearance model o eep he nowledge of raced objecs even if hey are parially hidden. The robusness and he reaciviy is based on a selecive updae process, ha manages differenly visible pixels, pixels occluded by saic or moving regions and pixels ha are no visible anymore, due o shape changes self-occlusions or sudden silhouee s moion. 10. ACKNOWLEDGEMENTS The projec is funded by he European Newor of Excellence DELOS of he VI Framewor Program. 11. REFERENCES [1] D. Beymer, K. Konolige, Real-ime racing of muliple people using coninuous deecion, In. Conf. on Compuer Vision, [2] I. Cohen, G. Medioni. Deecing and Tracing Moving Objecs in Video Surveillance Proc. of he IEEE CVPR 99, For Collins, June [3] R. Cucchiara, C. Grana, A. Prai, R. Vezzani, Compuer Vision Techniques for PDA Accessibiliy of In-House Video Surveillance in Proceedings of ACM Mulimedia Firs ACM Inernaional Worshop on Video Surveillance, Bereley (CA), USA, pp , Nov. 2-8, 2003 [4] R. Cucchiara, C. Grana, A. Prai, R. Vezzani, Probabilisic Posure Classificaion for Human Behaviour Analysis in press on IEEE SMC Transacions, Par A: Sysems and Humans, special issue on Ambien Inelligence, 2004 [5] R. Cucchiara, C. Grana, M. Piccardi, A. Prai, "Deecing Moving Objecs, Ghoss and Shadows in Video Sreams" in IEEE Transacions on Paern Analysis and Machine Inelligence, vol. 25, n. 10, pp , 2003 [6] Hariaoglu, D. Harwood, and L. S. Davis, "W-4: Real-ime surveillance of people and heir aciviies," IEEE Trans. PAMI (22) 8, pp , 2000 [7] M. Isard and A. Blae. A smoohing filer for condensaion. In Proc. ECCV, volume 1, pages , [8] S. Khan, M. Shah, Tracing People in Presence of Occlusion, Asian Conf. on Compuer Vision, Taiwan, Jan [9] A. J. Lipon, e al. Moving arge classificaion and racing from real-ime video IEEE Image Undersanding Worshop, 1998, pp [10] S.J. McKenna, e. al. Tracing ineracing people, IEEE In. Conf. on Auomaic Face and Gesure Recogniion, France, Mar 2000, pp [11] H.T. Nguyen, and A. W.M. Smeulders. Templae racing using color invarian pixel feaures. In Proc. ICIP'02, Vol 1, pp , Rocheser, [12] P. Pérez, C. Hue, J. Vermaa and M. Gangne. Color-based probabilisic racing. ECCV'2002, Copenhagen, Denmar, June 2002 [13] Hyung-Ki Roh, Seonghoon Kang, Seong-Whan Lee: Muliple People Tracing Using an Appearance Model Based on Temporal Color. ICPR 2000: [14] A. Senior, e al. Tracing people wih probabilisic appearance models, In. Worshop on Perf. Eval. of Tracing and Surveillance Sysems, [15] N.T. Siebel, S. Mayban, Fusion of Muliple Tracing Algorihms for Robus People Tracing, 7h European Conf. on Compuer Vision, Denmar, May 2002, vol. IV, pp [16] C. Sauffer, W.Eric, L. Grimson Learning Paern of Aciviy Using Real-Time Tracing IEEETrans on PAMI (.22)8, Augus 2000 [17] T. Zhao, R. Nevaia and F. Lv, Segmenaion and Tracing of Muliple Humans in Complex Siuaions, CVPR Kauai, Hawaii, Dec., 2001.