Online Learning of Region Confidences for Object Tracking

Transcription

1 Online Leaning of Region Confidences fo Object Tacking ABSTRACT This pape pesents an online leaning method fo object tacking. Movated by the attenon shifng among local egions of a human vision system duing tacking, we popose to allow diffeent egions of an object to have diffeent confidences. The confidence of each egion is leaned online to eflect the disciminave powe of the egion in featue space and the pobability of occlusion. The disbuon of egion confidences is employed to guide a tacking algoithm to find coespondences in adjacent fames of video images. Only high confidence egions ae tacked instead of the ene object. We demonstate feasibility of the poposed method in video suveillance applicaons. The method can be combined with many othe exisng tacking systems to enhance obustness of these systems.. Intoducon Object tacking is one of the peequisites fo analyzing and undestanding video data, and has been an acve eseach topic in the compute vision community ove the last two decades. The aim of tacking is to automacally find the same object in an adjacent fame fom a video sequence once it is inialized. The pevious eseach on object tacking falls into thee diffeent categoies: appeaance modeling, moon modeling, and seaching methods. Appeaance modeling epesents an object in an image by cetain featues fom video. Diffeent colo models, e.g., colo histogams, ae commonly used to chaacteize moving objects in images. Online selecon of colo spaces has been studied in object tacking []. Appeaance can also be simplified by using contous in tacked objects that can be easily segmented fom thei backgounds []. On the othe hand, pixel values ae widely used to epesent backgound objects, such as in inte-fame diffeencing scheme, Gaussian mixtue model ove me [3], adapve filte methods [4], minimal and maximal intensity value methods [5], DE level set [6], Hidden Makov models (HMMs) [7], and kenel density esmaon techniques [8]. A moon model pedicts an object s locaon in a new fame using its histoy. Linea models impose constaints that an object can only has tanslaonal o affine moons [9]. Non-linea models impose less constaint on moon Datong Chen and Jie Yang School of Compute Science Canegie Mellon Univesity {datong, yang+}@cs.cmu.edu than linea models, but they ae moe difficult to esmate and ae moe sensive to noise [0]. Seaching methods use vaious stategies to find an object, within an aea pedicted by a moon model, that is most simila to the appeaance of the tacked object in an adjacent fame of a video sequence. Apat fom the locaon, a seaching algoithm may also seach fo the most pope scale of the tacking taget. Many efficient seaching algoithms, such as mean-shift [], have been developed to seach local best matching of a igid o nonigid object. A Kalman filte tacks objects using both foegound and backgound moon models [3]. acle filteing is supeio to Kalman filteing using nonpaamec density esmaon and mulple hypotheses [4]. These thee appoaches, howeve, only addess the poblem of how to tack an object, unde the assumpon that what to tack is known. In this pape, we ae mainly inteested in invesgang anothe impotant poblem: what should be tacked? in a tacking pocess. In othe wods, ou goal is to impove object tacking by tacking selected local egions instead of tacking the ene object. Occlusions and complex backgounds (backgounds with simila colos as foegound objects) ae two majo challenges fo tacking an object in video. Many tacking eos ae encounteed when an object is tacked in font of a backgound that looks simila to the object in tems of tacking featues. Fo example, in Figue (ow ) the peson in the blue shit and white pants looks vey simila to the backgound with blue windows and white wall if the tacking algoithm depends on colo infomaon. Tacking eos may also occu because of occlusion. Even paal occlusions will somemes damacally change the appeaance of a tacked taget in a featue space, and theefoe incease the pobability of tacking failue. Howeve, a human vision system does not have any poblem pefoming pefect tacking in these cases. In pacce, vey often a paally occluded object sll contains enough disciminave tacking infomaon in its non-occluded egions elave to its suoundings. Recent psychology eseach esults [5] suggest the same, showing that a human vision system does shift its attenon among diffeent local egions duing the obsevaon of a moving object. Unlike the global focus of the attenon pocess, which has been applied to model human attenon [9] and moon gouping [0], this

2 subtle attenon shifng implies that diffeent local egions of an object should have dynamically diffeent confidences duing a tacking task. In this eseach, we pesent an appoach fo leaning the confidences of local egions of an object online duing tacking. We popose associang diffeent local egions of an object with diffeent confidences on the basis of thei disciminave powes fom thei backgound and pobabilies of being occluded. To this end, object appeaances ae fist accumulated using a layeed epesentaon. We then paon the object aea indicated by its associated laye into ovelapping egions with the same size. The confidences of these egions ae leaned online using the disnguish powe on the basis of matching distances and a Gaussian occlusion model, based on the disciminave powes and the occlusion pobabilies. The disciminave powe of a egion can be exploited between the egion and its local backgound []. The occlusion pobability of a egion is esmated fom the distances between font layes. We futhe popose thee altenave bias models to integate mulple egion confidences into a state-of-the-at tacking algoithm. Using these models, we illustate the advantages of using egion confidences against occlusions and a complex backgound. confidence shifts up when the bottom of the object is occluded. In the second example, tacking attenon is paid fistly to the middle of the object because the uppe and lowe pats of the object have simila colo to the suounding backgound. The confidence of the uppe body inceases as the object moving away fom the blue windows. The last image shows that a potenal occlusion fom an object (peson) on the ight educes the confidence of the egions on the ight side. Although the mechanism of this funcon in a human vision system is not completely known to psychologists, as compute sciensts we ae cuious to know if a funconally simila mechanism can be implemented in a compute vision system to impove object tacking. Cuent laye Local egions Tacking in next fame using attenon models Leaning egion confidences Update laye Single-focus model Mul-focus model Mul-focus template Figue An oveview of the poposed appoach. Figue Illustated high confidence egions in object tacking with occlusions and complex backgounds. The fist ow illustates that the cente of the high confidence egions shifts up when the bottom of the object is occluded. The second ow shows the changes of high confidence egions due to the backgound similaity and potenal occlusion.. A Method of Exploing Region Confidences Intuively, we believe that a tacking system may pay moe attenon to non-occluded egions o outstanding egions to tack an object against occlusion and a complex backgound. Recent psychology esults [5] have povided some scienfic evidence fo this pocess in a human vision system. Figue illustates two examples of the shift to egions with high confidence duing tacking. The fist example shows that the cente of high Figue shows the diagam of an appoach to chaacteize the ole of egion confidences in a tacking pocess. In the poposed appoach, we fist use a laye epesentaon to emembe the appeaance of an ene object. Thee ae two types of layes, font laye and backgound laye, which ae updated using tacking esults fom evey fame. Then, an object is tacked by focusing on some high confidence egions instead of the ene object. The confidences of the egions ae leaned fom the tacking esults in the pevious fame. Given locaon and scale of an object in the pevious fame, we paon the object and its suoundings with a sliding window into seveal ovelapping egions. The confidence of each egion is then computed accoding to its disciminave powe and pobability of being occluded. Afte obtaining the high confidence egions of an object, we map them to its latest laye model and cop thei appeaances. To combine mulple high confidence egions, we popose thee altenave spaal bias models.

3 Each of these spaal bias models can be integated into in a tadional tacking algoithm, fo example the mean shift algoithm. The locaon and the scale of the ene object ae then pedicted by using the elave posions of the tacked egions in its laye model. 3. Layeed epesentaon In ode to keep updang appeaances of an object in video fames, we build a laye fo each object in the foegound and a laye fo the backgound. Layeed epesentaon of a video was poposed by Wang et al. [6] and extended by many othe eseaches, including Daell et al. [7] and Tao et al. [8]. In this pape, a laye model of an object at me t is defined as Lt = ( Ot, At, Dt), whee O t is denoted as the locaon of an object in fame t. We assign this value as the geomec cente of the object. A t indicates the appeaance of the object, and D t encodes the depth of the laye in ou epesentaon. The backgound laye has a depth D t = 0. The depth of a font laye is always geate than but may vay duing a tacking pocess. 3.. Kenel density esmaon of a laye model Tadionally, the appeaance of a laye at me t should consist of only the object segmented fom backgound and occlusions. Howeve, a pefect segmentaon is difficult to obtain with occlusions. We use the kenel density esmaon (KDE) poposed by Elgammal [8] to avoid the difficulty of segmentaons in laye modeling. Given a set of appeaances A = ( At, A,..., ) t A t n of a laye extacted with ectangula windows fom n fames, we can nomalize the size of each appeaance and epesent it as A = ( At, A,..., t A tn ). Let At ( x ) be a pixel value at a locaon x in the ectangle appeaance patch of A t. Given the obseved pixel value At ( x ) in a tacking candidate window A t (can also be nomalized to A t ), we can esmate the pobability of this obsevaon as: n ( At ( x) ) = αik( At ( x), At ( x) i ), () n i= whee K is a kenel funcon defined as a Gaussian funcon: x x K( x, x) = e σ. () πσ The constant σ is the bandwidth. Using the colo values of a pixel, the pobability can be esmated as: j j ( At( x) At i ( x) ) n ( At ( x) ) = αi e σ,(3) n i= j ( R, G, B) πσ whee α i, i=,.n, ae the weights associated with appeaance samples in the model A, which can be computed as: αi = ( At i ( x) ). (4) A x At i 3.. Laye update Using the KDE technique, the laye update adapts the appeaance of model A using new samples. The model A needs to be inialized with only one example at the beginning of a tacking pocess. Appeaances of the tacked object ae added into A unl the numbe of samples eaches a pe-specified value n. We use n=5 in this pape. 4. Region confidence leaning Let us denote D θ as the matching distance between the appeaance of an object (laye L) and a hypnosis image aea θ in an object tacking algoithm. The matching distance is usually defined in a featue space against some types of vaiance in the object appeaance duing the tacking. Fo example, the colo histogam is obust to non-igid object moon. To apply the matching distance on local egions of the laye L, we divide a laye into m ovelapping egions as shown in Figue 3. Fo each egion, we denote C as its confidence, C =. We assume the matching distance of the ene aea is a linea combinaon of its local egions: D = C D, (5) θ θ whee D θ is defined as the matching distance of the egion. We futhe define the coect locaon of the object as θ 0. Since an opmal set of egion confidences should make the matching distance at the coect locaon D θ outstanding fom the othe locaons, we maximize 0 the sum of the squae of the diffeences of the matching distances fom locaons θ s to the θ 0 with espect to the confidence set C = { C }: ( Dθ Dθ ) = ag max C 0 ( Dθ Dθ0 ) ag max θ C θ.(6) Obviously, we should only choose one egion in the ideal case to obtain the best tacking esult. In pacce, we do not know the coect locaon θ 0 in a new video fame befoe tacking. Howeve, the values D θ and D θ can be 0

4 esmated fom the pevious tacking esult, when appeaances of the object and the backgound do not damacally change. sliding window colo histogam N N N N N N N N N N N N N Figue 3 Ovelapped egions of an object located by its associated laye. In detail, given the tacked locaon of the object in a pevious fame, we ae able to compute D θ and D θ 0 using the laye model and obtain the egion confidences. These confidences ae vey simila to the coesponding confidences in the cuent fame, though they ae not exactly the same. In ou expeiments, egions that contain small, non-igid moon appea slightly diffeent, as they ae in the laye model. Occlusions may also change the appeaances of egions damacally. It is not vey obust if we only keep one egion as in an ideal case. It is somemes even hade to find an outstanding egion in an object laye, especially when local egions ae too small. The confidences theefoe need to be esmated in a moe obust way. To this end, we conside that a egion s confidence C is elated to pedicted disciminave powe and the pedicted pobability of being occluded. Fom Eq (7), the disciminave powe of a local egion is expected to have a high value if the sum of the squae of the diffeences of the matching distances between the hypnosis locaons θ s and the tue locaons θ 0 is high. We thus define a funcon f that select egions with the highest pedicted disciminave powe of the egion t using the D θ and D θ fom the object laye and latest 0 fame. Let us define f as the selecve funcon based on the disciminave powe of the local egions. The method of leaning the funcon f is discussed in the Secon 4.. Apat fom the pedicted disciminave powe, we also need to conside occlusions that may intoduce appeaance changes when egions in a laye model ae used to pedict the object appeaance in a new fame. Occlusions may completely change the appeaance of some egions. Let us denote the pobability of egion being occluded O. We use O as a weight in esmang the egion confidence. Theefoe, the confidence can be esmated as: C = α f βo, (7) ( ) N N N N N Figue 4 Sampling the egions aound a moving font laye. The egions containing in the laye ae labeled as posive examples () and the egions suound the laye ae labeled as negave examples (N). whee α is a nomalize to keep the sum of the confidences equal to. The paamete β is a scala to amplify the pobability O into the ange of [0, ]. 4.. Leaning the disciminave powe Although selecng only one egion is not always paccal, we sll expect to select as few egions as possible to avoid unnecessay computaon. Theefoe, the disciminave powe funcon f should be able to select as few egions as possible while keeping the disciminave powe stong and obust enough to tack an object in a new fame. We fist limit outside aea N to the suoundings of the object, which is usually the same as the seaching ange of a tacking algoithm. As we paon an object aea (aea ) into local egions, we may accidentally paon some backgound aea (aea N) into local egions as shown in Figue 4. The disciminave powe of a candidate egion in the aea is the matching distances fom this egion to evey the egions in the N aea, which is defined as the weighted sum of the matching distances: nei D( nei, ) nei N Dist() =, (8) nei nei N whee the matching distance funcon D is the same as the colo histogam matching distance used in Eq. (5) and the distance nei is denoted as the distance between the centes of the two egions. To select egions using the Dist values, we model the disbuon of the Dist values of all egions in the aea as Gaussian: Dist ( ( )) Gµ (, σ ). The egions with high confidences ae then selected the theshold: τ = µ + σ,

5 which means that the selecve funcon f is defined as: Dist( ) > τ f = f() (9) = 0 othewise Vaious numbes of egions can be selected fom diffeent appeaances of objects. 4.. Occlusion pobability The occlusion pobability of a egion should be highe if it is close to anothe font laye. Fomally, if two layes L and L ae close enough, which means the distance between the two locaons L L < ε, we define the occlusion pobability of a egion in the L using the neaest Gaussian distance: nei O = e σ, (0) πσ whee nei is the closest egion in the laye L to the egion. The distance nei is denoted as the distance between the centes of the two egions. The vaiance σ is elated to the pedicted moving speed of the object. Now, we can compute the confidence of each egion using Eq. (7). 5. Tacking objects using egion confidences In ode to illustate the poposed idea without losing geneality, we integate the poposed online leaning mechanism into the mean-shift algoithm []. That is, we tack objects using spaal-biased colo histogam as the appeaance featue fo each object. The bias is computed on the basis of the egion confidences. Given a laye L and its egion confidences C t the tacking of the laye L in a subsequent fame can be descibed as the following thee iteave steps: Table Tacking algoithm with egion confidence. Compute a bias fo each posion in the laye L using a specific bias model;. Compute spaal-biased colo histogam of the latest appeaance of the laye L using the biases; 3. Tack the laye L using spaal-biased colo histogam in the new fame; Tadional colo histogam-based algoithms, as we used in the Secon 4, conside each pixel as the same weight o adial weights. The spaal-biased colo histogam calculates the colo disbuon of a egion with diffeent bias at each pixel. We model this bias ove an object aea as the combinaon of the leaned egions confidences. Thee diffeent bias models ae pesented hee. 5.. Single focus model (SFM) A human vision system tends to have one focus at a me. Accodingly, we would like ou model to focus on only one point at a me. The bias educes as distance inceases fom the focus. Let Loc() t be denoted as the elave cente locaon of the egion T of a font laye L. We define the focus F(L) as the aveage locaon of the centes of all local egions weighted by thei confidences: F( L) = C Loc( t ). () L, i C = L, i= We model the weights of elave posions in the laye as a Gaussian disbuon in which highe weights ae given to the posion close to the focus. Fo each posion x in the laye L, the bias of x is taken fom: ( ) wx ( ) N FL ( ), σ s. () The single focus model is vey obust to occlusions because only one local pat of the object needs to be tacked. Howeve, the focus may somemes fall into a egion that has vey low confidence due to the aveaging step. This usually happens when many high-confidence egions suound a egion with low confidence. To addess this poblem, we popose a mul-focus model. 5.. Mul-focus model (MFM) The mul-focus model woks like the vision systems of bees o othe insects, which have many simple eyes woking togethe. We ceate a mul-focus model using a Gaussian mixtue model, in which each local egion with non-zeo attenon is modeled as a Gaussian disbuon. Following the definions in the last secon, the weight of a posion x in the laye L can be computed as: wx ( ) = πσ C m L, i= L, i= i x Loc( ) σ m Ce. (3) 5.3. Mul-focus template (MFT) The thid focus model we popose is a template. We simply put a theshold δ on the attenon of the local egions, and give each pixel in theshold egions an equal weight. Fomally, the bias of a posion x in the laye L is: η x t& C wx ( ) t > δ =, (4) 0 othewise whee η takes the value that keeps the sum of the bias of all posions into.

6 6. Expeiments We fist compaed the matching distance disbuon with and without using egion confidence. The matching distances ae computed aound the manually labeled gound tuth. To show the values at all possible locaons, we pefom a full seach, though in pacce no tacking algoithm will eally seach so many locaons. Figue 5 shows matching distance disbuons computed fo tacking thee objects. We evese the distance values so that the centes can be seen in 3D sufaces. In each example we extact 35 egions and 8 N egions fo confidence leaning. Example (3) Radial bias E=33 SFM E=76 MFM E=69 MFT E=047 Example () Radial bias E=389 SFM E=50 MFM E=495 MFT E=60 Figue 5 Thee examples (in columns) of matching distance (evesed fo display) disbuons computed aound the gound tuth locaons in tacking. In each example, we epot the esults by using diffeent bias fom left to ight: convenonal adial, single-focus model, mul-focus model, and mul-focus template. (Diffeent examples may use diffeent colo anges) Example () Radial bias E=96 SFM E=44 MFM E=693 MFT E=7 The evaluaon value E is deived fom the Eq. (6), with the biased colo histogam. E = D D. (5) θ ( θ θ ) 0 In the fist example, we ae tacking a small object (30x pixels) that is occluded by anothe object. The MFT has the best chance to povide coect tacking accoding to the E value. Howeve, the disbuons of the fou methods look vey simila. The second example is obtained by tacking a bigge object (90x36 pixels) in font of a backgound that contains simila colos. The MFT shows not only the best E value but also a smooth disbuon. The MFM also pefoms bette than the othe two models in this case. The object we tacked in the thid example has simila size to the second one but with a athe simple backgound. The MFT povides the best E value and also a shape disbuon. To demonstate the advantages of the poposed tacking appoach, we pesent tacking examples with occlusions and complex backgounds.

7 Mean shift with adial model Single focus model (SFM) Mul-focus model (MFM) this example is caused by a 540-fame-long, close body contact (such as hand shaking and hugging) within an 40-fame-long inteacon between the two people. The video has 30 fames pe second with 360x40 pixels pe fame. Moon models cannot easily pedict the locaon of the peson because he has thee diffeent moon pattens in this example: walking quickly to the othe peson, vey small moon duing the geeng, and walking back slowly back to whee he began. Figue 6 pesents the tacking esults of using the standad meanshift algoithm and the mean-shift algoithms with the poposed attenon models (no backgound subtacon has been used.) The most diffeent esults ae the scales of the peson in the 5 th and the 6 th fames. The standad mean-shift algoithm can coectly tack the peson at the vey beginning of the encounte, but soon makes a typical eo of meging the two pesons as one object. Afte using the single focus model in the mean-shift algoithm, we obtain a moe accuate locaon fo the peson duing the encounte. The mul-focus model makes the same tacking mistake as the standad mean-shift algoithm. The eason is that the tacked peson is small and it is difficult to tune the vaiance σ m in Eq. (6). If the vaiance is too small, the featue does not have enough disciminave powe. A lage vaiance leads to a vey flat attenon disbuon, and achieves a simila tacking esult to the standad mean-shift. The mul-focus template does not have this difficulty with size, and obtains accuate tacking esults. We display the egions with high confidences in the mul-focus esults. The locaons of these egions futhe illustate how the high confidences shift in the occlusion. Mean-shift with adial model Mul-focus template (MFT) Figue 6 An example of a long peiod occlusion. The standad mean shift algoithm and MFM missed the accuate locaons of the peson and meged the two people togethe duing the occlusion peiod. The SFM and MFT povided moe obust tacking. We display the egions with non-zeo confidences fo the poposed models. The fist example pesents the challenge of tacking a peson occluded fo a long peiod of me by anothe peson in a coido. Unlike a shot-me occlusion caused by two objects moving acoss each othe, the occlusion in Mul-focus template (MFT) Figue 7 Tacking in complex backgounds using egion confidences. The mean-shift algoithm lost objects because the backgound has simila colos. The second example, Figue 7, shows a tacking challenge with complex backgounds. The standad mean-shift algoithm fails to tack the peson because the backgound has simila colo to the object. By using

8 egion confidences, a mean-shift algoithm impoved by the MFT can tack the peson in this case. The un cost of the egion confidence leaning is not vey high compaed to mean-shift tacking. It depends closely on how many egions ae used in leaning. As we used only 53 (35 and 8 N) egions, we can tack one object at 0 fames/second. 7. Conclusions In this pape, we have pesented an appoach to exploit local egion confidences fo tacking objects against paal occlusions and complex backgounds. In the poposed appoach, object appeaances ae updated using KDE layes. We have discussed the esmaon of the confidences of local egions of an object in the ideal case and poposed an online leaning method to obtain the egion confidences based on two factos: the disciminave powe of the egion in a featue space and the pobability of being occluded. We have poposed a soluon to model the disciminave powe and egion selecon using matching distances between objecon egions and neighbohood backgound. The pobability of being occluded is chaacteized as a Gaussian on the distance between the object and othe fontal layes. To make the egion confidence mechanism open to state-ofthe-at tacking algoithms, we have poposed thee special bias models to integate egion confidence infomaon. We have evaluated the poposed egion confidence leaning and special bias models using the disbuon of the matching distance in the featue space. Expeiments have demonstated the obustness of the integaon of the poposed appoach with the mean-shift tacking algoithm in tacking people against paal occlusions ove a long peiod of me and backgounds with simila colos. We have also compaed the tacking pefomance of using thee diffeent bias models. Expeiments have shown that, although some of them should theoecally pefom bette, in pacce they may lead to wose esults (fo example, the MFM.) Simple models, such as the MFT and SFM, can gain moe benefit fom the spaal bias tacking appoach. Fo the esults of tacking mulple objects, please see the enclosed video. The poposed appoach is easily integated into othe exisng tacking methods as an impovement fo occlusions and complex backgounds. As an example, we have integated it into a mean-shift algoithm by simply modifying the computaon of the colo histogam. We did not invesgate tempoal constaints on the tanslaon of the spaal bias in consecuve fames, which might povide inteesng knowledge to impove tacking. This will be ou futue effot. 8. Refeences [] R. T. Collins and Y. Liu. On-line selecon of disciminave tacking featues. ICCV 003. [] A. Galata, N. Johnson, and D. Hogg. Leaning vaiable length Makov models of behaviou. Compute Vision and Image Undestanding, 8(3): , 00 [3] W. E. L. Gimson and C. Stauffe. Adapve backgound mixtue models fo eal-me tacking, CVR, pp [4] K. Toyama, J. Kumm, B. Bumitt, and B. Meyes. Wallflowe: inciples and pacce of backgound maintenance. oc. of the ICCV, pp. 55 6, 999. [5] I. Haitaoglu, D. Hawood, and L. S. Davis. W 4 - a eal me system fo detecon and tacking people and thei pats. oc. of the FG, Naa, Japan, 998. [6] N. aagios and R. Deiche. A DE-based Level Set Appoach fo detecon and tacking of moving objects. Technical Repot 373, INRIA Sophia Anpolis, 997. [7] B. Stenge, V. Ramesh, N. aagios, F. Coetzee, and J. Bouhman, Topology fee hidden makov models: Applicaon to backgound modeling, oc. of the ICCV pp , 00. [8] A. Elgammal, R. Duaiswami, D. Hawood, L.S. Davis. Backgound and foegound modeling using nonpaamec kenel density esmaon fo visual suveillance. oc. of IEEE, 7(90) pp. 5 63, 00. [9] B.D. Lucas and T. Kanade, An Iteave Image Registaon Technique with an Applicaon to Steeo Vision, oc. of DARA Image Undestanding Wokshop, pp. -30, 98. [0] C. Davatzikos, J.L. ince, and R.N. Byan. Image egistaon based on bounday mapping. IEEE Tans. on Medical Imaging, 5(), pp.- 5, 996. [] D. Comanicu, V. Ramesh, and. Mee. Real-me tacking of non-igid objects using mean shift. In oc. CVR Vol (), pp. 4-49, 000. [] G. R. Badski. Compute vision face tacking fo use in a peceptual use inteface. Intel Tech. Jounal, Q, 998 [3] A. Gelb. Applied Opmal Esmaon, MIT ess, Cambidge, MA 974 [4] M. Isad and A. Blake. CONDENSATION condional density popagaon fo visual tacking. IJCV Vol 9(), pp. 5-8, 998. [5] W. M., Shim and. Cavanagh. Attenon shift induced by appaent moon can cause posion compession. Jounal of Vision, 4(8), 575a, 004. [6] J. Y. A. Wang and E. H. Adelson. Layeed epesentaon fo moon analysis. In poc. of CVR pp: , 993. [7] T. Dael and A. entland. Coopeave obust esmaon using layes of suppot. In IEEE T-AMI 7: , 995. [8] H. Tao, H. S. Sawhney, R. Kuma. Object tacking with Bayesian esmaon of dynamic laye epesentaons. IEEE T-AMI Vol. 4(), pp: 75-89, 00 [9] R. Sefelhagen, J. Yang and A. Waibel. Esmang focus of attenon based on gaze and sound. ACM oc. wokshop on ecepve use intefaces, pp: -9, 00. [0] K. Toyama and G. Hage. Incemental focus of attenon fo obust vision-based tacking, IJCV Vol. 35:(), pp: 45-63, 999.