Collaborative Tracking of Objects in EPTZ Cameras

Transcription

1 Collaboratve Trackng of Objects n EPTZ Cameras Fasal Bashr and Fath Porkl * Mtsubsh Electrc Research Laboratores, Cambrdge, MA, USA ABSTRACT Ths paper addresses the ssue of mult-source collaboratve object trackng n hgh-defnton (HD) vdeo sequences. Specfcally, we propose a new jont trackng paradgm for the multple stream electronc pan-tlt-zoom (EPTZ) cameras. These cameras are capable of transmttng a low resoluton thumbnal (LRT) mage of the whole feld of vew as well as a hgh-resoluton cropped (HRC) mage for the target regon. We explot ths functonalty to perform jont trackng n both low resoluton mage of the whole feld of vew as well as hgh resoluton mage of the movng target. Our system detects objects of nterest n the LRT mage by background subtracton and tracks them usng teratve coupled refnement n both LRT and HRC mages. We compared the performance of our jont trackng system wth that of trackng only n the HD mode. The results of our experments show mproved performance n terms of hgher frame rates and better localzaton. Keywords: Collaboratve trackng, EPTZ trackng, Mean-shft analyss 1. INTRODUCTION Automatc object detecton and trackng from vdeo sequences plays an mportant role n modern vson-based systems. The applcatons of ths technque are mmense ncludng automatc vdeo survellance 6, aeral magery 2, sports analyss 1, actvty analyss 3, etc. The ablty to spatally locate targets of nterest and track the movng targets over a perod of tme s of central mportance n these tasks. Most of the past and ongong research n ths area has been focused on trackng from sngle statc or movng camera. Recently, the research efforts have focused more on the problem of collaboratve trackng n the dstrbuted envronment 4. One am of the vson systems employng ths approach s to cover a large feld of vew for trackng multple targets at a fxed resoluton by usng multple cameras. An added advantage s that trackng can be performed n severe occlusons under some homography constrants 5. A lot of modern vson system applcatons are hghlghtng the mportance of both wde coverage area as well as sharp detal on the movng target. One example of mult-sensor object detecton and trackng s n vdeo survellance tasks. Here the goal s wde area montorng on one hand, and acqurng hgh-qualty bometrc mages on the other hand 6. To dentfy people at a dstance, a hghly zoomed mage s needed. But wth hgh zoom, only a small porton of the area under survellance can be montored. To address ths problem, Zhou et al 6 propose a master-slave archtecture system. A statc, wde FOV (master) camera s used to montor wde area and detect movng humans. Upon detectng a human n FOV, the actve narrow FOV pan-tlt (slave) camera s used to acqure hgh-resoluton mage of the human target and to perform trackng n narrow FOV. Ther system s bult usng three standard PC systems for master camera processng, slave camera processng and pan-tlt unt control. On smlar lnes, Mgdal et al 7 propose wde area hghresoluton survellance usng a statc wde FOV and an actve narrow FOV PTZ camera unt. Ths focus-of-attenton camera system s shown to cover a wde area for survellance at hgh-enough resoluton to perform movng object detecton and trackng. It s shown n ther presentaton that for an equvalent level of hgh-resoluton target trackng acheved by the statonary and PTZ camera system, a network of around 100 fxed FOV cameras wll be requred n one partcular applcaton settng studed by them. Another major applcaton area s sports vdeo analyss. An example applcaton s presented by Needham et al 1 for ndoor soccer player trackng. In ths doman, there s a growng nterest n performng automatc play and player analyss. From the perspectve of sports scence ndustry, knowledge of players movement patterns durng play s an mportant benchmark for educaton and tranng. Sports broadcast ndustry s also nterested n generatng more dynamc content for end-users to more closely watch the movements and tactcs of ther favorte players. For most broadcast sports, the playng feld s a farly wde area whch can not be covered by sngle camera systems wth hgh-enough resoluton. Ths observaton s the motvaton of our approach for collaboratve trackng of sports players usng EPTZ * fath@merl.com, Telephone:

2 cameras. In ths paper, we present a system for sem-automatc player detecton and automatc trackng n the context of base-ball vdeo. Our approach s doman-ndependent and can be appled to wde-area survellance task; ths pont s llustrated by trackng results n outdoor survellance applcaton. Work presented n ths paper s a step towards the ultmate goal of generatng hgh resoluton tracked magery of players usng sngle ETPZ camera. Ths paper s organzed as follows: secton 2 lays out the problem doman and applcaton scope ncludng trackng and background modelng algorthms used n our mplementaton; secton 3 presents our EPTZ soluton wth specfc system outlne; secton 4 detals the results of collaboratve EPTZ trackng n our applcaton doman; fnally, conclusons and future work s presented n secton PROBLEM STATEMENT AND BACKGROUND Ths paper addresses the followng problem: Gven the HD vdeo data of a play on a sport feld (specfcally base-ball n our case), perform player detecton and trackng generatng hgh-resoluton magery of the desred players as pcked by the end-user. More specfcally, we deal wth a practcal stuaton of the problem formulated heren: Due to massve amounts of data to be transferred, the HD camera and assocated bandwdth s not capable of delverng HD qualty mages (1280x720) at full frame rate. Instead, at HD Mode the camera can delver a low-resoluton mage of the whole FOV as well as a hgh-resoluton mage of the target regon. We provde a soluton that works under these constrants to delver hgh-resoluton tracked magery of the sem-automatcally detected players n the feld. Any system for player detecton and trackng n sports vdeos has to deal wth several challengng problems. The frst problem s mantanng a wde area of coverage and a hgh-resoluton mage of the tracked player at the same tme. These two requrements, as noted n the prevous secton, are at odds wth each other. The second problem s automatcally detectng the objects of nterest (players) for further trackng. Ths ssue s further complcated by the rather cluttered background n sports envronments, owng manly to the audences outsde the feld. Apart from the cluttered background, another factor that contrbutes to further complcate the ssue s the uncontrolled llumnaton changes and weather effects n the outdoor envronments. Fnally, once the objects of nterest have been successfully detected spatally, an object trackng algorthm s needed to temporally locate the object n the vdeo sequence frame by frame. Ths paper ntends to address these three ssues n the context of trackng movng objects n HD vdeo sequences from an EPTZ camera Wde Area Coverage at Hgh Resoluton The tradtonal soluton for wde-area vdeo survellance s to set up enough number of narrow FOV statc cameras n a collaboratve network. Wth the constrant of obtanng hgh-resoluton magery of players (movng targets) n sports (vdeo survellance) applcaton, ths amounts to just scalng up the exstng solutons to work on massve amounts of data. A soluton based on smply scalng up the exstng approaches s far from practcal n most applcatons. In our applcaton for baseball player trackng at HD1 resoluton of 1280x720 pxels, around 16 HD cameras wll be needed to tle the base-ball feld the way t s covered by our system. Ths massve amount of data prohbts any exstng soluton due to the sheer volume. The requrements for data transmsson and storage alone are prohbtve n modern applcatons. To address ths problem, prevous approaches have concentrated on developng statc and PTZ cameras n master-slave archtecture 6,7. The dsadvantage of that approach s that the two cameras have to be carefully calbrated to same world coordnate system. We address ths ssue n the context of modern cameras that support mage outputs at multple resolutons albet wth some tme lag. We observe that recently, very hgh resoluton cameras that are able to accommodate full frame rate vdeo have become avalable n the vson research market. These cameras can provde more than 1Mega-pxels resoluton and delver exceptonal detals of the depcted scene. However, data transmsson bandwdth and computatonal bottlenecks often lmt the amount of vdeo data to be analyzed at the user end for most exstng systems. To accomplsh the wde area of coverage at hgh-resoluton, such cameras offer a mode of transmsson that supports two vdeo streams. One stream corresponds to a low-resoluton thumbnal (LRT) of the over-all feld of vew, whle the second stream delvers a hgh-resoluton cropped (HRC) vew of the target. In other words, the hghresoluton cropped mage acts as an electronc pan-tlt-zoom (EPTZ) camera. Further detals of our soluton are provded n the next secton Object Detecton usng Background Modelng The problem of automatcally detectng the objects of nterest for further trackng has been wdely addressed n the recent lterature. One approach for achevng ths goal s usng areas of moton n the scene to dscrmnate them from the

3 background. Towards ths goal, several successful approaches have been proposed that buld a statstcal representaton of the background. A bref tranng perod s requred where statstcs from a few frames are used to model the background appearance. Once the background model has been establshed, the ncomng frames are compared wth ths model to mark the pxels belongng to movng objects. The background model should be robust to varatons n background resultng from multple tme-varyng natural phenomena. The varatons n scene background arse from dfferent sources, such as smooth and sudden llumnaton changes, wndy (stagnant) condtons resultng n hgh (low) moton of natural objects lke trees, waves n water, etc. To counter ths problem n a robust manner, most of the exstng approaches for background modelng rely on statstcal representatons. In ths representaton, the random process at each pxel from multple frames s assocated wth a probablty densty functon (pdf). The per-pxel pdf for background model can be represented parametrcally usng a specfed statstcal dstrbuton that fts the data well. Alternatvely, non-parametrc approaches could be used for ths representaton. Ths stochastc representaton of the random process at each pxel models the varous appearances of the background effectvely. On the lnes of parametrc background modelng, Stauffer and Grmson 8 proposed modelng the background wth a mxture of Gaussans. The pxel-wse mxture of Gaussan approach models varous forms of backgrounds effectvely. Ther background update method makes use of expectaton maxmzaton- (EM-) based framework for background learnng. The background model update s performed at a pre-specfed learnng rate to dynamcally adjust to changng condtons. Elgammal et al 9 argued to use non-parametrc methods for densty estmaton to represent arbtrary dstrbutons n a data-drven manner. They used kernel densty estmaton at each pxel to represent dfferent background states. Our background modelng technque s based on recursve Bayesan learnng as proposed by Porkl et al 10. In ths approach, the background model s smlar to Stauffer s pxel-based adaptve mxture model. The recent hstory of each pxel, { x 1,x 2,,xt}, s modeled by a mxture of K Gaussan dstrbutons. The probablty of observng the current pxel value s: K P x x,, (1) ( t) = ω,t η ( t µ,t Σ,t) k = 1 where K s the number of mxture components, ω,t s an estmate of the mxture weght of th Gaussan n mxture at tme t, µ,t s the mean value of the th Gaussan n mxture at tme t, Σ,t s the covarance matrx of the th Gaussan n mxture at tme t, and η represents the Gaussan probablty densty functon: 1 η ( x, t µ, Σ ) = e n 1 2 Σ 2 ( 2π ) 1 T 1 ( xt µ t ) Σ ( xt µ t ) 2 A choce of 3 5 for the parameter K s found to be suffcent for most applcaton. For more dynamc scenes, more layers are requred. In our formulaton, nstead of usng EM for learnng the parameters of the mxture, we use Bayesan recursve learnng approach. Here, each pxel s defned as a layer of 3D multvarate Gaussans. In the RGB color space, each layer corresponds to a dfferent appearance of the pxel. Usng the Bayesan approach, we are not estmatng the mean and varance of each layer, but the probablty dstrbuton of mean and varance. The background update algorthm mantans the multmodalty (varous appearances) of the background. At each update, at most one layer s updated wth the current observaton. After background model learnng and update, foreground objects are detected by computng Mahalanobs dstance of each pxel s observed color wth confdent background layers. Pxels that are outsde of 99% confdence nterval of all confdent layers of the background are consdered as foreground pxels. Fnally, connected components labelng s performed on foreground pxels to mark the movng targets to be tracked Trackng of Detected Objects The problem of relably trackng the objects of nterest becomes a lot smplfed once foreground regons based on background models have been detected, and a change detecton mask at each frame s generated. In the classcal object trackng settng, a manually ntalzed object s to be tracked over tme n a vdeo sequence. Whereas, n our case, the results of object detecton after background subtracton combned wth user-nput are used for object ntalzaton. Gven two vews of the scene (wde-area at low-resoluton and narrow-area at hgher-resoluton), we hghlght that jont trackng mposes certan unque requrements over the choce of trackng algorthms and whch vew to use. Dependng on the orgnal FOV sze and amount of subsamplng nvolved, the objects of nterest could be very small to perform any meanngful trackng n the LRT vew. On the other hand, because more data s avalable n the HRC vew, more relable (2)

4 trackng performance can be acheved n ths vew. Also, because of better resoluton at the target, better object model update can be performed usng data from HRC. Trackng n the HRC vew has ts own problems though. Snce the HRC vew gves a zoomed verson of the object beng tracked, ts FOV s dmnshed. So, movng objects do not spend much tme n the FOV spanned by HRC vew. Also, snce the object sze to FOV sze rato s qute hgh n ths vew, a hghmoton tolerant trackng algorthm s needed. As far as background generaton and update s concerned, there seems to be lttle choce. Mantanng a hgh-resoluton background model at the HD resoluton s a tme consumng task whch becomes prohbtve n real-tme system requrements. Due to these unque requrements, we use mult-kernel mean-shft trackng algorthm wth foreground regons mask generated through background modelng n the HRC vew. Also, the background mage generated through LRT vew s mantaned usng HRC vew. In the next secton, we te the peces together n the form of collaboratve trackng system Mult-Kernel Mean-Shft Trackng wth Foreground Mask Mean-shft s a real-tme algorthm for target trackng based on object appearance model. The trackng s based on a robust clusterng technque whch does not requre pror knowledge of the number of clusters or ther shapes. The algorthm starts on the data ponts and at each teraton, moves n the drecton of maxmum gradent. Iteratons end when the pont converges to a local mode of the dstrbuton 12. As ponted out earler, the orgnal mean-shft algorthm requres sgnfcant overlap on the target kernels n consecutve frames. Ths condton mght not be met n the hgh moton areas of sports vdeos. To address ths ssue, we use the mult-kernel mean-shft algorthm 11 for trackng n the HRC vew. In ths approach, multple kernels wthn a fxed radus of the orgnal object locaton are ntalzed at hgh moton areas. Object template lkelhood scores are computed at the converged ponts and the locaton assocated wth maxmum score s marked as the object locaton. Gven the outlne of detected object from change detecton mask, the mult-kernel mean-shft frst forms an object model for matchng n successve frames. The object model s a nonparametrc color template n the form of W H D matrx whose elements are 3D color samples from the object. W and H are wdth and heght of the object respectvely and D s the sze of the hstory wndow. Let Z 0 be the ntal locaton of the object obtaned through sem-automatc player ntalzaton. If the object has been tracked up to current frame, t corresponds to the estmated locaton obtaned N through trackng from prevous frame. We refer to the foreground pxels nsde the estmated target box as ( x,u ) = 1, where x s the 2D coordnate n the mage coordnate system and u s the 3D color vector. Correspondng foreground M,D sample ponts n the template are represented as ( y,v ). Let { } m j jk q j = 1,k = 1 s be the kernel weghted color hstogram of the s= 1 template of ntalzed player to be tracked usng multvarate normal kernel k N for weghtng. Let p(z) be the color hstogram of the canddate centered at locaton Z and let b(z) be the background hstogram at the same locaton. We construct background color hstogram usng only the confdent layers of the background. The smlarty between object model (template of player beng tracked) and the canddate regon s measured usng modfed Bhattacharya coeffcent. Ths smlarty measure ncludes background nformaton: m ( z) = f qsps( z) b bs( z) ps( z) (3) ρ α α s= 1 s= 1 where α f and α b are weghts for foreground and background pxels. To locate the object n next frame, mean-shft vector at locaton Z 0 then becomes: m ( ) m z = n 2 x z0 N ( 0 ) = 1 x z.w.g 0 n 2 x z0 w.g N = 1 h h (4)

5 HD HD HD Camera LRT Process -BG Generaton - Player Int LRT Process -BG Update LRT Process -BG Update HRC HRC Process -Track -Object Model Update -Background Update HRC Process -Track -Object Model Update -Background Update Fgure 1. Ths fgure shows the orgnal HD mage n the camera as well as the two mages LRT and HRC transferred to system for processng. * * where g N ( x ) k N ( x ) =, and w are the mean-shft weghts derved through Bhattacharya smlarty defned n Eq. (3) and h s the bandwdth of spatal kernel. Next, we compute the probablty that a sngle pxel ( x,u ) nsde the canddate target box centered at z belongs to the object. We compute ths usng Parzen wndow estmator on color dstance between current target box and the object template hstory: ( ) l u = Dh 2 D 1 u vjk 3 k N (5) c k = 1 h c where h c s the bandwdth of 3D color kernel, set to be 16 n our experments. The fnal combned lkelhood of an object beng at locaton z s measured as: L z l u k N = 1 1 N x z h 2 ( ) = j( ) N (6) The kernel k N assgns smaller weghts to samples farther from the center of the object template makng the estmaton more robust. Object model update s handled n the HRC tracker snce we have a lot more pxels to update the object model at hgh resoluton. At the tme of each update, the oldest samples of each pxel of the template (at D th slce) are replaced wth new ones. Based on foreground segmentaton, template pxels correspondng to background pxels n current frame are not updated. Fnally, scale adaptaton of the objects s performed usng the foreground pxels. Let B be the boundng box of the object centered at estmated locaton z 1. We defne a second box O around the object center whch has twce the area of B. The object scale s the soluton of the maxmzaton: ( ) ( 1 ˆ( )) S = cˆ x + c x x B x O B 1, x foreground ĉ( x) = 0, x background The optmal boundng box of object s stored for object localzaton n the next frame. (7) 3. ELECTRONIC PTZ SOLUTION As brefly descrbed n the prevous secton, the man challenge n our hgh-resoluton player trackng s dealng wth the mages at two dfferent resolutons effectvely. Mantanng a robust background n the wde FOV usng LRT mage, we nsure wde-area coverage of our system. At the same tme, trackng n the HRC mage allows us to generate hgh

6 resoluton magery of the tracked player for end-user dsplay. Also, hgh resoluton background mage s mantaned usng nformaton from LRT background and successve HRC mages. An advantage of ths multple-resoluton approach usng the EPTZ camera s that the homography between low-resoluton and hgh-resoluton scene s trvally known. The hgh-resoluton verson of the target s merely from a scaled and cropped regon of the low-resoluton scene. Thus, unlke master-slave archtecture of conventonal mechancal PTZ camera trackng, no camera calbraton step s requred n the case of EPTZ camera-based system. Get Next LRT Image Get Next LRT and HRC Images Generate/Update Background Generate/Update Background Model (LRT Only) NO User-Input YES YES Player Ext/Lost Track n HRC Mean-Shft NO Update Object Model usng LRT Mask and HRC pxels YES User-Input NO Fgure 2. Flow chart of the algorthm to process LRT and HRC mages from camera. It also hghlghts the role of sem-automatc player ntalzaton and collaboratve trackng System-Camera Interacton The nteracton between HD camera and our collaboratve trackng system s shown n Fg. 1. The camera nternal buffer stores HD frames at each tme nstant. The dotted horzontal lne n the center of the fgure shows the boundary between the camera nternal buffer and what t shares wth the system outsde. The dotted rectangle on HD mage shows hypothetcal regon requested by our system and to be delvered by the camera (after an expected delay of a few frames). The LRT mage delvered by the camera reaches our system as next frame, but the HRC mage requested by the system mght arrve after a fnte amount of delay. Please note that background update s performed n LRT to enable semautomatc player ntalzaton and to assst Mean-shft trackng n HRC mage. The object model, however s updated n the HRC vew only, as more pxels are avalable n ths vew and thus the object model can be updated more confdently wth the help of background-foreground mask. An example of varous mage components of the system s shown n Fg.

7 3. The hgh-resoluton background mage at the same resoluton as orgnal HD mage s shown n (a). An LRT mage s shown n (b), whle an HRC mage s shown n (c) after detecton and trackng. (a) (b) (c) Fgure 3. Collaboratve HD player detecton and trackng on base-ball sequence (1280x720). (a) HD background mantaned from the low resoluton background and ndvdual hgh resoluton mages. (b) Low resoluton thumbnal (LRT) mage of the whole FOV. Please note the very small object szes. (c) EPTZ hgh resoluton cropped (HRC) mage of a detected player (ptcher) beng tracked System Archtecture A software operatonal scenaro of our system s shown n Fg. 2 n terms of algorthm flow-chart. Our system starts by grabbng the LRT mage of the scene. At ths tme, background generaton s performed, whch spans a few frames accumulatng the scene background statstcs. An automatc player ntalzaton s possble wth ths approach, but the sem-automatc approach relyng on end-user nput s preferred. The man reason for ths approach s that the end-user gets to select the player they want to see more closely n a feld wth tens of potental players to be tracked. For trackng n the hgh-resoluton vew, we use mean-shft algorthm because of ts real-tme performance. One problem wth the mean-shft algorthm for trackng s that the mean-shft kernel requres suffcent object overlap between successve frames to be tracked. There are two ssues n our applcaton doman that exacerbate ths problem. Frstly, snce the HRC mage comes from a narrow FOV, the hgh-speed players beng tracked spend very short amount of tme n ths vew. Secondly, there s a fnte but non-neglgble tme lag t between the tme that exact HRC boundng

8 box s requested from the camera and the tme when t s delvered to the outsde system from camera. Ths tme lag could be as hgh as around 10 frames. These ssues call for a trackng approach that s more tolerant to object moton on the scale that object could leave the wdth of the spatal kernel entrely resultng n trackng error. Under these constrants, the orgnal mean-shft algorthm wll not provde good trackng n ths vew. Ths problem s overcome by our mult-kernel mean-shft algorthm wth background nformaton. Snce multple search kernels are ntalzed for object localzaton wthn a radus, the mult-kernel approach s found to be a lot more tolerant to frame delays and hghspeed motons n narrow FOV mages. Once trackng has been performed, bject model update s handled n the HRC tracker snce we have a lot more pxels to update the object model at hgh resoluton. At the tme of each update, the oldest samples of each pxel of the template (at D th slce) are replaced wth new ones. Based on foreground segmentaton, template pxels correspondng to background pxels n current frame are not updated. Fnally, the scene background mage generated through LRT mage s updated based on new HRC nformaton and object locaton Fgure 4. Collaboratve HD player trackng n HRC vew on base-ball sequence (1280x720). EPTZ trackng result mages for hgh resoluton dsplay at end-user sde wth correspondng frame numbers. 4. RESULTS We have tested our collaboratve object detecton and trackng system on a few vdeo sequences. Results are presented on an HD vdeo sequence example from the EPTZ camera capturng baseball game. Expermental results from an outdoor vdeo sequence are also presented. The baseball sequence shows a lot of background nose due to audences constantly movng, cameras flashng and other tme varyng llumnaton changes. Also, several scenes of occluson are present whch coupled wth the fact that unforms of several players appear the same present major problem n robustly trackng the object over tme. Fg. 4 shows HRC mages from a player beng tracked. Please compare the amount of hgh-resoluton nformaton n these mages wth that of Fg. 3(b). It s apparent that our collaboratve trackng approach presents a lot sharper detals on the tracked target albet at the same computatonal cost of dealng wth much less amount of data. Please note also the robustness of trackng system to object shape deformatons (frame 325), occlusons (frames 458 and 480) and scale changes (between frames 49 and 443). Results from outdoor vdeo sequence are presented n Fg. 5. The hgh-resoluton background mage s dsplayed n (a) whch s the same sze as HD mage n camera. A low resoluton thumbnal mage s shown n (b). Dfferent mages from a trackng stuaton are shown n (c). All mages are shown to the scale.

9 (a) (b) (c) Fgure 5. Collaboratve HD human trackng n outdoor envronment. (a) HD background mage mantaned through low resoluton background and ndvdual hgh resoluton cropped mages. (b) Low resoluton thumbnal (LRT) mage of the whole FOV. (c) EPTZ trackng result mages for hgh resoluton dsplay at end-user sde wth correspondng frame numbers. Ths vdeo sequence also underlnes the robustness of collaboratve trackng system to changes n object scale as object moves farther from the camera, as well as severe occluson. The automatc detecton system based on background generaton s also robust to gradual and sudden llumnaton changes due to weather condtons to a certan extent. Fnally, we present the results of system performance n terms of average processng tmes per frame. These results are reported n table 1 for our collaboratve detecton and trackng system. For comparson, we also report the results of processng the orgnal frames n HD resoluton. As can be seen from ths table, the collaboratve trackng framework n EPTZ scenaro, results n performance mprovement of more than an order of magntude. Table 1. Average per frame processng tmes for trackng n our collaboratve soluton as compared to HD only trackng. Collaboratve LRT+HRC HD Only Background Update 50 msec. 800 msec. Trackng 20 msec. 25 msec. Mscellaneous 5 msec. 10 msec. Total 75 msec. 835 msec.

10 5. SUMMARY AND CONCLUSIONS In ths paper, we have addressed the ssue of detectng and trackng objects of nterest from HD vdeo sequences. The approach s motvated by modern HD cameras wth specal mode of operaton to conserve transmsson and processng bandwdth. In ths mode of operaton, the camera transmts a low resoluton thumbnal mage of the whole fled of vew at sgnfcantly less resoluton as compared to the HD mage t captures. In conjuncton wth that, these cameras provde a hgh resoluton cropped mage from a sgnfcantly less FOV. Ths electronc pan-tlt-zoom (PTZ) settng effectvely lets the camera perform as a combnaton of a wde FOV statc camera and a narrow FOV actve camera unt. We present a background generaton and object trackng system based on ths operatonal scenaro for hgh frame-rate object detecton and trackng. Expermental results are reported on an HD sequence from baseball vdeo, and outdoor vdeo sequence. Future work wll focus on computatonally effcent means for generatng and updatng hgh-resoluton background. Also, the ssue of trackng n low-resoluton n conjuncton wth hgh-resoluton trackng needs to be explored. ACKNOWLEDGMENTS The authors would lke to thank Oncel Tuzel for helpful nsght and mplementaton. REFERENCES 1. C. J. Needham and R.D. Boyle, Trackng Multple Sports Players Through Occluson, Congeston and Scale, Brtsh Machne Vson Conference, Manchester, UK, F. Raf, S. M. Khan, K. Shafq and M. Shah, Autonomous Target Followng by Unmanned Aeral Vehcles, SPIE Defence and Securty Symposum 2006, Orlando FL. 3. F. Bashr, W. Qu, A. Khokhar and D. Schonfeld, HMM-based Moton Recognton System usng Segmented PCA, IEEE Internatonal Conference on Image Processng, Genoa, Italy, W. Qu, D. Schonfeld and M. Mohamed, Dstrbuted Bayesan Multple Target Trackng n Crowded Envronments usng Multple Collaboratve Cameras, EURASIP J. Appled Sgnal Processng (In prnt). 5. S. M. Khan and M. Shah, A Multvew Approach to Trackng People n Crowded Scenes usng a Planar Homography Constrant, 9 th European Conference on Computer Vson ECCV 2006, Graz, Austra, X. Zhou, R. T. Collns, T. Kanade and P. Metes, A Master-Slave System to Acqure Bometrc Imagery of Humans at Dstance, ACM Internatonal Workshop on Vdeo Survellance, Nov J. Mgdal, T. Izo and C. Stauffer, Movng Object Segmentaton usng Super-Resoluton Background Models, Workshop on Omndrectonal Vson, Camera Networks and Non-Classcal Cameras, Oct C. Stauffer and E. Grmson, Adaptve Background Mxture Models for Real-Tme Trackng, n Proc. IEEE Conf. on Computer Vson and Pattern Recognton, Fort Collns, CO, Vol. II, 1999, pp A. Elgammal, D. Harwood and L. Davs, Non-parametrc Model for Background Subtracton, n Proc. European Conference on Computer Vson, Dubln, Ireland, Vol. II, 2000, pp F. Porkl and O. Tuzel, Bayesan Background Modelng for Foreground Detecton, ACM Internatonal Workshop on Vdeo Survellance and Sensor Networks (VSSN), Nov. 2005, pp F. Porkl and O. Tuzel, Mult-Kernel Object Trackng, IEEE Internatonal Conference on Multmeda and Expo, July 2005, pp D. Comancu, V. Ramesh and P. Meer, Kernel-Based Object Trackng, IEEE Transactons on Pattern Analyss and Machne Intellgence (PAMI), Vol. 25, No. 5, pp , 2003.