A ROBUST FEATURE TRACKER FOR ACTIVE SURVEILLANCE OF OUTDOOR SCENES

Transcription

1 Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, 2003 A ROBUST FEATURE TRACKER FOR ACTVE SURVELLANCE OF OUTDOOR SCENES G. L. Forest and C. Mchelon Departement of Mathematcs and Computer Scence (DM) Unversty of Udne, Va delle Scenze, 206, Udne, TALY e-mal: {forest,mchelon}@dm.unud.t Receved 12 March 2002; accepted 9 January 2003 Abstract n ths paper, we propose a robust real-tme object trackng system for outdoor mage sequences acqured by an actve camera. The system s able to compensate background changes due to the camera moton and to detect moble objects n the scene. Background compensaton s performed by assumng a smple translaton (dsplacement vector) of the background from the prevous to the current frame and by applyng the well-known tracker proposed by Lucas and Kanade. A reference mage contanng well trackable features s mantaned and updated by the system at each frame. A new method s appled to reject badly tracked features. The current frame and the background after compensaton are processed by a change detecton method n order to locate moble objects. Results are presented n the contest of a vsual-based survellance system for montorng outdoor envronments. Key Words: Actve Vson, Feature trackng, Object Detecton, Vdeo and mage Sequence Analyss, Vdeo-Survellance. 1 ntroducton Detecton and trackng of movng objects are mportant tasks for computer vson, partcularly for vsual-based survellance systems [1],[2]. The applcaton of vdeo-survellance has an hgh range of purpose, from traffc montorng [3] to human actvty understandng [4]. Vdeo survellance applcatons, most tmes, mply to pay attenton to a wde area, so dfferent knds of camera are generally used; e.g. fxed cameras [2], omndrectonal cameras [5] or moble cameras [6],[7],[8],[9]. n the proposed system, a pan, tlt, zoom (PTZ) camera wth tuneable parameters (.e., a camera whch can change the vewpont, for example to keep a target n the center of the mage, or modfy ntrnsc parameters lke focus or black level compensaton) has been used. Moton detecton s generally consdered a dffcult task f mage sequences are acqured by a movng camera [7],[8],[9]. n fact, when comparng two consecutve frames of a sequence, dfferences Correspondence to: forest@dm.unud.t, mchelon@dm.unud.t Recommended for acceptance by Francsco Perales ELCVA SSN: Publshed by Computer Vson Center / Unverstat Autonoma de Barcelona, Barcelona, Span

2 22 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, 2003 n pxel ntenstes occur n the whole mage, snce the ego moton of the camera causes an apparent moton of the statc background. A number of moton detecton methods for movng camera sequences was proposed n the lterature. Murray and Basu [7] use a background compensaton technque based on the calculaton of the background moton from the camera pan and tlt angles; ths technque allows just rotaton of the camera about the lens center. Most of the methods proposed n the past are based on the compensaton of the background moton followed by a frame-by-frame change detecton. Recently, ran and Anandan [8] address the problem of movng object detecton n mult-planar scenes estmatng a ''domnant'' 8-parameters transformaton. Arak et al. [9] proposed a background compensaton method based on the estmaton of the background moton. Ths s acheved by trackng some feature ponts on the background and estmatng the parameters of an affne transformaton from prevous frame to actual frame; they construct snakes around the bnary mask of the detected changng ponts. n ths paper, we propose a new real-tme moton detecton technque, based on the well known Lucas-Kanade tracker [10], for a vsual-based survellance systems. The proposed method s focused partcularly on the determnaton of a set of well trackable features and on the computaton of the dsplacement vector. As t uses a reference mage for mantanng nformaton about well trackable features, t dffers from the technques adopted tll now. n [11], Tommasn et al. appled a feature rejecton rule to avod the use of bad features n computaton of the medan of sngle feature dsplacements. So, the accuracy of the dsplacement estmaton depends on the goodness of the feature rejecton rule. n [9], the heurstc adopted conssts n the determnaton of set of three features whose affne transformaton parameters are optmal. Ths requres the computaton of the parameters for an uncertan number of tmes, that s n antthess wth a real-tme constrant. The approach proposed n ths paper s based on the followng steps: (a) determnaton of a reference mage contanng well trackable features; (b) selecton of well trackable features from the reference mage by avodng the calculaton at each step of the features on the whole mage; (c) trackng of the features by the Tomas and Kanade-tracker [12]; (d) estmaton of the dsplacement vector due to the camera moton for background compensaton; (e) applcaton of a change detecton process to locate moble objects. 2 Method descrpton As shown n Fgure 1, the proposed method s based on a frame by frame moton detecton technque. Let (x,t ) be the -th frame of the mage sequence. When the frst frame (x,t 0 ) s acqured a reference mage contanng all well trackable features s bult up. Let Feat(t ) be the feature mage computed at the tme nstant t. These features are selected accordng to the method proposed by Tomas and Kanade [12], whch s based on the computaton of the egenvalues of a 2x2 matrx contanng the partal dervatves of the current mage computed on a wndow W. Only features whose both egenvalues are hgh are consdered well trackable features [13]. Let S be the set of well trackable features. The proposed updatng process permts to have, at each frame t, the poston of all well trackable features n the mage wthout a new computaton of the features already present n Feat(t ). Gong on wth the sequence frames, the actual set of features s used to fnd a feature correspondence between pars of consecutve frames. The Tomas - Kanade tracker [12] s appled on the feature set to obtan the feature postons n the current frame. A new method s proposed n ths paper for dsplacement estmaton n order to reach two dfferent objectves: reference mage updatng and background compensaton.

3 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, The updatng process s composed of two parts: (a) rejecton of bad tracked features and (b) ntroducton of new features belongng to new mage regons. Feature rejecton s a necessary task snce some feature errors occur durng the trackng phase due to the dstorton ntroduced by the camera moton. All the features whose dsplacement dffers from the one estmated by the proposed method are consdered bad trackable features and they are elmnated from S. A problem can occur after ths operaton: the number of features could be too low for a robust trackng[14]. The soluton proposed n ths paper conssts on the repopulaton of S by ntroducng new well trackable features. Ths operaton allows to use the tracker always on a suffcently large set of features. Fgure 1 - System descrpton The background compensaton operaton translates the current frame by the estmated dsplacement vector d. Let (x+d,t ) be the current frame after compensaton. A change detecton operaton [15], s appled between (x+d,t ) and (x,t -1 ). The output mage s a bnary mage whose whte pxels correspond to ponts belongng to movng objects. Black pxels represent statc ponts n the scene. System outgong nformaton s used n a vdeo-based survellance systems for outdoor scenes. The proposed system s able to detect, to classfy and to track an object by mantanng t at the center of the mage, and regulatng the camera's parameters n order to mprove at each frame the qualty of the acquston process.

4 24 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, Feature extracton and selecton The proposed method uses a reference mage Feat(t ) contanng the canddate features from whch the trackable feature set s extracted. Two dfferent steps are requred for the reference mage usage: (a) ntalzaton and (b) updatng. 3.1 ntalzaton The ntalzaton step conssts n the constructon of Feat(t 0 ) contanng the good features that wll be used by the tracker. The reference mage s bult at the frst frame of the sequence. The method proposed by Sh and Tomas [13] s appled to the frst frame n order to extract all the well trackable features. Let λ 1 and λ 2 be the egenvalues of the 2x2 matrx G,.e., 2 x x y G = (1) 2 W x y y where = x and = y are the partal dervatves respectvely n the x and y drecton, and x y W s a small mage wndow centered on the pont (x,y) where the feature s computed. A feature s consdered well trackable f the followng condton yelds : mn(λ 1,λ 2 )>λ (2) where λ s a predetermned threshold [13]. n Fgure 2, the reference mage computed on a real mage s shown. (a) (b) Fgure 2 - Map representaton (b) of the real mage (a)

5 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, Updatng When Feat(t 0 ) has been ntalzed, features are selected on the frst frame. Ths process s based on the poston of the features present n Feat(t 0 ). Then, selected features are used to estmate the dsplacement vector d between the current and the prevous frame. Assumng that the dsplacement vector s calculated accurately, the reference mage Feat(t -1 ) of the prevous frame s multpled by the dsplacement vector d to obtan Feat(t ). Features belongng to regons no more present n the new mage (x,t ) are elmnated. Moreover, as the camera moton ntroduces n the current mage (x,t ) new regons, Feat(t ) must be updated. The fnal Feat(t ) s computed as followng: Feat ( t ) = Feat( t 1) d + Features( ( x, t ), d) (3) where Features((x,t ),d) s the functon that calculates the good features on the new regon generated by the camera moton, calculated by equaton (2). Ths method allows to save a lot of computatonal tme elsewhere spent n egenvalues calculaton for the current frame. Only neghbourhood regons of features relatve to the camera moton are analysed. 4 Moton Estmaton The proposed tracker has been desgned to operate n outdoor scenes, wth dfferent lght condtons and n real-tme. These constrants have requred some mprovements wth respect to exstng technques [7],[9],[13]. 4.1 Feature selecton Real-tme trackng forces to work wth a low number of features. Consequently only few features, classfed as good by equaton (2), must be selected and consdered n S. Moreover, f all features belongng to S are located on a small regon of the mage (.e., S conssts of few neghbourhood ponts), features could be tracked badly due to nose, occlusons, or smply because they are out of the mage. To avod ths problem, only approprate features should be selected. Ths selecton s performed n two steps. n the frst step, a feature f s extracted; n the second one, all neghbourhood features of f are nhbted from next selecton. The neghbourhood of a feature f conssts n a crcle wth center on f and radus equal to a prefxed threshold R th. On a real envronment R th depends on the complexty of the scene. 4.2 Feature Trackng n ths Secton, the Sh-Tomas-Kanade tracker [12], used n our system, s brefly descrbed. Gven an mage sequence (x,t), f the frame rate s hgh enough (.e., 15 frame/s) wth respect to the changng n the scene, we can assume that for small regons only a translaton movement occurs: ( x, t) ( x + d, t + τ ) (4)

6 26 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, 2003 where τ s the tme acquston rate. Gven a feature wndow W, we want to fnd the dsplacement d whch mnmzes the sum of squared dfferences: [ ε = ( x + d, t + τ ) ( x, t) ] (5) w 2 Usng Taylor-seres expanson, we obtan: T ( x + d, t + τ ) ( x, t) + d + τ (6) t By mposng that the dervatves of ε wth respect to d are zero, we obtan: w 2 x x y x y 2 y d = τ w t x y (7).e., Gd = e (8) As the equaton (5) s an approxmaton, the procedure has to be repeated yeldng a type of Newton-Raphson teraton scheme [13]. 4.3 Dsplacement estmaton Once S s bult, the feature trackng algorthm s used on t. The output s a correspondence relaton among features n the current frame (x,t) and n the prevous frame (x,t-1) whch s used to compute the dsplacement. Unfortunately, often t occurs that some features of S are not well tracked due to presence of nose, occlusons etc. n order to face ths problem t s necessary to dstngush features tracked well from the others. A feature s consdered well tracked f ts dsplacement d f corresponds to the real mage dsplacement d. The strategy followed to determne the whole mage dsplacement s to defne a relablty factor for the dsplacement of each feature present n the set S. For each feature f, a resdual error E f s normalzed n order to lmt the effects of ntensty changes between frames, by subtractng the average grey level for each wndow [11]: E = ( J ( + d) J ) ( ( x) 2 [ x )] (9) P W

7 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, where J ( ) = (, t + τ ), ( ) = (, t) and J, are the average grey levels of the two regons consdered. The relablty factor s then calculated by addng all resdual errors of the features havng the same dsplacement and weghtng the result by dvdng t by the number of the nterestng features: RF f D ( D ) = D D E f D (10) where E f s the resdual of the feature calculated by the equaton (9), D s the set of all dsplacements comng out from S. The dsplacement, wth the lowest relablty factor, s selected as estmated ego-moton. By constructon the dsplacement vector selected s the vector whose features have the mnmum mean error and ther number s maxmum for all mnmum RF dsplacement: d = D RF( D ) = mn { RF( D )} (11) D D After havng estmated the dsplacement for background compensaton, t s necessary to update S. Ths process s performed n two steps. At the frst step, all features not well tracked are rejected. At the second step, the remanng features are analysed for evaluatng whatever they satsfy the condton of beng a good feature to track (accordng to equaton (2)). Tommasn et al. [11] reduce the problem of detectng bad features to a problem of outler detecton based on an effectve model-free rejecton rule, X84. Ths rule employees medan and medan devaton and than t rejects values whch are more than k tmes the Medan Absolute Devatons (MADs). The threshold k s selected by expermental tests. When the cardnalty of S becomes small (e.g., lower than 20), a new problem occurs: n presence of a feature really bad tracked, the value of the MAD becomes enough bg to avod the rejecton of ths feature. n order to face ths problem, we propose a new rejecton rule whch takes as nput the set of features used for dsplacement estmaton. The mean and standard devaton are calculated as follows: µ = f D D E f [ E f µ ] f D σ = (12) D 2 All the features whose resdual s bgger than (µ+kσ) are dropped out from the set, where k s computed as suggested n [11]. 5 Background Compensaton The background compensaton operaton conssts n translatng the current frame (x,t) by the estmated dsplacement vector d. Snce statc pxels are n the same poston n the mage (x+d,t) and n the prevous frame (x,t-1), pxels that assume dfferent poston can be assocated wth movng objects. A change detecton operaton can be appled between the prevous frame (x,t-1) and the

8 28 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, 2003 translated frame (x+d,t). The threshold used n the change detecton operaton s selected automatcally accordng to the rule defned by Sndaro and Forest n [15]. Let B(x,t) be the bnary mage resultng from the change detecton operaton. n presence of an hgh frame-rate, B(x,t) wll contan the edges of the movng objects (movng edges [7]). As shown n Fgure 3, background compensaton s a necessary operaton; wthout t the change detecton returns a very nosy mage. A correct background compensaton addresses to a more useful mage for moton detecton. n the case of a statc envronment, the result of the change detecton on two consecutve frames after compensaton, obtaned wth the proposed method, conssts n a vod mage. The presence of movng objects n the scene ntroduce blobs n the B(x,t) mage. n partcular, the blobs that appear n the B(x,t) mage are obtaned by a logcal AND among the blobs generated by movng objects n the prevous and current frame. Fgure 3 - Compensaton results 6 Expermental Results The proposed method has been tested on sequences acqured on outdoor envronments. n partcular, a parkng area around the Unversty of Udne has been selected as test ste. Several sequences have been acqured by varyng the pan and/or the tlt parameters of the camera. An ncremental complexty of the scenaros has been consdered rangng from statc scenaros to scenaros n whch one or more objects are movng. The expermental results are completed by testng the

9 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, proposed approach on mage sequences acqured by changng the zoom, the camera moton speed and the settngs of the ntrnsc camera parameters (.e., focus, aperture, etc.). 6.1 Camera Setup The sequences used for experments are acqured by a Cohu 3812 CCD camera mounted on a Robosoft Pan-Tlt Unt (PTU ). n Fgure 4, a scheme of the PTZ-CAMERA system s shown. A Matrox METEOR- PC board frame-grabber has been used for mage acquston and a 1.2Ghz PC-BM compatble has been used to run the system. Table A contans the specfcatons of the Computer-Controlled PTU Fgure 4 - PTZ Scheme PAN TLT POSTON RANGE (-180,+180 ) (-80,+31 ) RESOLUTON SPEED RANGE (1.59 /sec, 149 /sec) (0.39 /sec, 37 /sec) Table A PTU techncal specfcaton

10 30 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, 2003 Table B shows the man camera parameters and PTU speed used to acqure dfferent test sequences. Pan Speed [degree/sec] Tlt Speed [degree/sec] Zoom Autofocus Sequence x ON Sequence x ON Sequence x ON Sequence x OFF Sequence x OFF Sequence x OFF Sequence x OFF Table B Camera parameters and PTU speed used n the experments. 6.2 Tested Scenaros All the sequences have been acqured from the 2 nd floor of the Unversty buldng, near to ffteen meters from the ground, but the frst three sequences n a dfferent place from the last four. Multple parameters have been selected to verfy the algorthm effcency. Frst the module dsplacement dfference MDD has been consdered. t represent the Eucldean dstance from the estmated vector d and the real mage dsplacement rd (the dsplacement that mnmze the compensaton error): ( d rd ) + ( d rd ) 2 x 2 x y y MDD = (13) Then, the compensaton error CE has been computed as percentage of statc pxels n the change detecton mage: CE = N N x= 1 y= 1 B( x, y, t) N N (14) Those two parameters represent a qualty measure of the dsplacement estmaton algorthm. Robustness of the system needs two more parameters: (a) the number of good features rejected (GFR) as the percentage of features rejected that would be consdered well trackable (.e. whose egenvalues respect the equaton (2))and (b) the number of bad features mantaned (BFM) as percentage of features not rejected that would be consdered not well trackable. For each of these parameters the mean µ and the maxmum value Max over the entre sequence has been calculated.

11 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, Frst Scenaro: no movng objects. Ths scenaro contans all those results derved from peces of sequences wthout any movng object. Ths s the smplest scenaro snce no feature occluson occurs. Results of a pan camera movement, of a tlt camera movement, and of a jont pan-tlt movement are shown n Tables C, D and E respectvely. Second Scenaro: one or more movng objects. Ths part of the experments conssts on testng the system on all the sequences contanng one or more movng objects. The problem complexty s ncreased from the frst scenaro snce a new problem occurs. The system can select as a good feature to track a pont belongng to the movng object. n ths case, the feature s reject by the system. The results are shown n Tables F,G and H. Both scenaros have been selected from the seven sequences whose characterstcs are explaned n Table B. Each experment has dfferent speed camera movements, zoom and focus settngs. PAN MDD CE GFR BFM TLT MDD CE GFR BFM Mean Max Table C Mean Max Table D PAN & TLT MDD CE GFR BFM Mean Max Table E PAN MDD CE GFR BFM Mean Max Table F TLT MDD CE GFR BFM PAN & TLT MDD CE GFR BFM Mean Max Table G Mean Max Table H The result shown n Tables C-H should be dscussed by dvdng them n two categores: (a) parameters related to the process of dsplacement estmaton and (b) parameters related to the process of rejectng the features consdered no stll good. The frst class of results shows a really good estmaton of the dsplacement. Over 10 4 frames computed, there s only one case n whch the MDD error estmaton s equal to two pxels (see Table

12 32 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, 2003 E). Ths demonstrate that the proposed method obtans a good dsplacement estmaton, and consequently allows to obtan a good change detecton. The CE parameter s always lower than about 0.08%. Ths mply that on 10 5 statc pxels, after the compensaton process and the change detecton operaton, only 80 pxels are erroneously consdered no statc by the system. The proposed method, estmatng accurately the dsplacement, allows a good detecton of moble pxels. Ths result s possble thanks the ablty of the proposed method to mantan a good feature set on whch to apply the trackng algorthm. The set S s correctly updated thanks to the correct rejecton of all those features that are not good for trackng. The second class of parameters, we have consdered, shows that the system has a behavour more orented to reject good features than to mantan wrong ones. The hgher percentage of the parameter GFR respect to those relatve to BFM demonstrate ths. t s better to reject features that could be good for trackng rather than mantanng bad features for the next step. The values of these two parameters could result more clear observng that the mnmum number of features n S used n the experments s equal to nne. The mean value of features rejected at each step s equal to three. Thus rejectng only one good feature mply a GFR factor equal to The experments have shown that there s not any correlaton between the process of the dsplacement estmaton and the ntrnsc camera parameters selecton. Sequences acqured wth autofocus or wth a fxed focus value nvolve the same system behavour. The changes to the zoom parameter cause only the modfcaton of the threshold λ thr. By reducng the zoom, a lower number of background objects s acqured, so a lower number of features s detected. Decreasng the value of λ thr, the number of features detected ncreases and the system can operate as wth the wder zoom condton. 6.3 Comparsons wth other approaches The proposed approach has been compared wth the methods proposed by Tomas and Kanade [12] and by Tommasn et al. [11]. The comparson has been done on the Module Dsplacement Dfference computed on the same frames (about 10 4 ) used n the prevous experments. Table shows the obtaned MDD values. KANADE TOMASN PROPOSED MDD Mean Max Table t s worth notng that the method proposed works better than others. Ths s due to the fact that a low number of features has been used. n partcular, the Tomas Kanade method shows hgh values for both mean and max parameters. The approach proposed by Tommasn et al. reduces the MDD error of about 50%. Fnally our proposed method reduces the mean value to 0.2 (wth a reducton factor of 7 wth respect to Tommasn method and 11 wth respect to Tomas Kanade method) and the max value to Lmts of the system

13 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, The man lmt of the system s represented by stuatons n whch t s mpossble to select a set of well trackable features. Ths s the case n whch, for example, the zoom s to hgh and the mage contans a movng wde object n a close up shot. The number of statc pxels s to low and ther poston s always at the mage bounds. No features can be extracted and the method cannot be appled. The soluton of ths stuaton could be to reduce the zoom untl a certan number of features could be extracted. A second lmt s represented by those sequences n whch the object moves and the background s unform (e.g., a wall). Agan the system cannot be appled because any feature can be extracted from statc background. 7 Conclusons n ths paper, a system able to compensate background changes due to the camera moton and to detect moble objects n outdoor scenes has been proposed. The nnovatve parts cover two man problems: (a) estmatng n a robust way the dsplacement occurrng between two consecutve frames and (b) speedng up the task regardng the mantenance of a relable feature set over whch the tracker proposed by Tomas and Kanade s appled. Expermental results have been presented n the contest of a vsual-based survellance system for montorng outdoor envronments. Seven dfferent sequences, each one acqured wth dfferent parameters( zoom, tlt and pan camera speed, focus and aperture) and ncreasng complexty (related to the number of movng objects) have been consdered. Over 10 4 frames computed, the proposed method obtans a good dsplacement estmaton: on 10 5 statc pxels, after the compensaton process and change detecton operaton, only 80 pxels are erroneously consdered no statc by the system. The proposed approach has been compared wth other feature trackng methods [11],[12] and the obtaned results show a sgnfcance reducton of the MDD error. REFERENCES [1] C.S. Regazzon, V. Ramesh, G.L. Forest, Specal ssue on vdeo communcatons, processng, and understandng for thrd generaton survellance systems, Proceedngs of the EEE, Vol. 89, No. 10, October [2] G.L. Forest, P. Mahonen and C.S. Regazzon, Multmeda Vdeo-Based Survellance Systems: from User Requrements to Research Solutons, Kluwer Academc Publshers, September [3] D. Koller, K. Danlds, H. H. Nagel, Model-based object trackng n monocular sequences of road traffc scenes. nternatonal Journal of Computer Vson, Vol.10, 1993, pp [4] S.L. Dockstader and T. Murat, Multple camera trackng of nteractng and occluded human moton, Proceedngs of the EEE, Vol. 89, No.10, Oct 2001, pp [5] J. Gluckman and S. Nayar. Ego-moton and omndrectonal cameras, EEE nternatonal Conference on Computer Vson, Bombay, nda, January 3-5, 1998, pp [6] J. Davson,. D. Red, and D. W. Murray. The actve camera as a projectve pontng devce, 6 th Brtsh Machne Vson Conference, Brmngham, September, 1995, pp [7] D. Murray, A. Basu, Moton Trackng wth an Actve Camera, EEE Transacton on Pattern Analyss and Machne ntellgence, Vol. 16, No. 5, May 1994, pp

14 34 G.L. Forest et al. / Electronc Letters on Computer Vson and mage Analyss 1(1):21-34, 2003 [8] M. ran, P. Anandan, A Unfed Approach to movng Object Detecton n 2D and 3D Scenes, EEE Transacton on Pattern Analyss and Machne ntellgence, Vol.20, No.6, 1998, pp [9] S. Arak, T. Matsuoka, N. Yokoya, H. Takemura, Real-Tme Trackng of Multple Movng Object Contours n a Movng Camera mage Sequence, ECE Trans. nformaton and Systems., Vol. E83-D, No.7, July 2000, pp [10] B.D. Lucas, T. Kanade, An teratve mage Regstraton Technque wth an Applcaton to stereo Vson, 7 th nternatonal Jont Conference on Artfcal ntellgence, Vancouver, August 1981, pp [11] T. Tommasn, A. Fusello, E. Trucco and V. Roberto, Makng Good Features Track Better, EEE Conf. on Computer Vson and Pattern Recognton, Santa Barbara, Ca, June 1998, pp [12] C. Tomas, T. Kanade, Detecton and trackng of pont features, Techncal report CMU-CS , Carnege Mellon Unversty, Pttsburgh, PA, Aprl [13] J. Sh, C. Tomas, Good features to track, EEE conference of Computer Vson and Pattern Recognton, Seattle, WA, June 1994, pp [14] J. Lpton, H. Fujyosh, and R. S. Patl, Movng target classfcaton and trackng from realtme vdeo, n Workshop Applcatons of Computer Vson, Prnceton, NJ, Oct. 1998, pp [15] L. Sndaro and G.L. Forest, Real-Tme Thresholdng wth Euler Numbers, Pattern Recognton Letters, Vol. 24, n press.