CONSISTENT VEHICLES TRACKING BY USING A COOPERATIVE DISTRIBUTED VIDEO SURVEILLANCESYSTEM

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1 Internatonal Research Journal of Appled and Basc Scences 2013 Avalable onlne at ISSN X / Vol, 4 (12): Scence Explorer Publcatons CONSISTENT VEHICLES TRACKING BY USING A COOPERATIVE DISTRIBUTED VIDEO SURVEILLANCESYSTEM Peyman Babae *1, Mahmood Fathy 2, Reza Berang 2 1. Department Of Computer, Scence And Research Branch, Islamc Azad Unversty, Tehran, Iran 2. Computer Faculty, Unversty Of Scence And Technology, Tehran, Iran *Correspondng Author emal:p_babae@eee.org ABSTRACT:Ths paper proposes a method of consstent vehcles trackng usng a dstrbuted cooperatve mult-camera survellance system.vehcles matchng n vews of dfferent cameras s a fundamental ssue for ncreasng the accuracy of consstent trackng. Among the most mportant research n ntellgent transportaton systems (ITS), automatcally urban traffc survellance s one of the crtcal and challengng tasks. A sngle-camera system has nherent lmtatons such as lmted feld of vew and errors n background subtracton. The proposed work addresses synchronzng the cameras for trackng vehcles smultaneously n overlappng felds of vew. Multple cameras wth overlappng felds of vew can provde fault tolerance and robustness for ssues such as vehcles occluson. Dstrbuted fundamental purpose s to effcently reduce the transmsson rate and also analyze a traffc scene and report statcs and nformaton of nterests. In ths manner, the survellance area s wder and usng multple vews of cameras to produce unform trackng can enhance the capablty and performance of consstent trackng. By presentng anew transmsson protocol n a robust manner, we evaluate the trackng accuracy. By ths way, n occluson stuatons, the vehclesn overlappng felds of vew are tracked more accurately. Keywords:Dstrbuted Mult-Camera Systems, Overlappng FeldsOf Vew, Consstent Trackng. INTRODUCTION Mult-camera systems become ncreasngly attractve n machne vson Applcatons nclude mult-vew vehcles trackng, behavor and event detecton, occluson handlng and etc. For many applcatons, there may be constrants of transmsson bandwdth and complexty n analyzng a huge amount of data centrally. In ntellgent transportaton systems (ITS), the convenent condtons are acheved by usng autonomous agents makng decsons n a decentralzed manner. In ths paper, we develop a method for trackng and recognton by a traffc vdeo survellance system of two dstrbuted cameras wth an overlappng feld of vews where processng and decson s dstrbuted across the cameras. The number of cameras and complexty of survellance systems have been ncreasng to make better coverage and accuracy. Also, multple cameras wth overlappng feld of vews can enhance the capablty of vson applcaton and provdng fault tolerance. In urban traffcs, a survellance system does not requre pror knowledge about the events that should be recognzed. The event models are automatcally bult by the system tself n an unsupervsed way. A commonly used approach n ths case s based on the clusterng the trajectores of the detected movng objects, then obtaned clusters are used as a normalty model for abnormalty detecton. An abnormalevent s smply dfferent from themost common patterns. Vehcle trackng s a challengng problem, especally n the presence of occluson. But n ths paper, we focus on the stage of vehcles trackng and analyss n a cooperatve dstrbuted vdeo survellance framework n urban traffc management (Mathew and Krshna, 2007). Ths paper s organzed as follows: an overvew of the prevous researches s presented n secton 2. Our proposed trackng algorthm s presented n secton 3. Expermental results of performance measurements wth respect to Ground-truth are presented n secton 4. Fnally the concluson s presented.

2 Intl. Res. J. Appl. Basc. Sc. Vol., 4 (12), , 2013 PrevousResearches On Mult-Camera Trackng Systems Prevous researches have been proposed to mplement mult-camera survellance systems. Multple cameras wth overlappng felds of vew can enhance the capablty and performance of trackng and provdng robustness for vehcles occluson. When a vehcle s occluded n one camera vew, other cameras may have a better vew of that vehcle. One approach s fully calbratng the cameras wth consderng all cameras parameters. Ths work s done wth projecton equatons usng coordnates of a set of ponts and ther projectons (Kelly et al., 1995). By usng kalman flters and sends the features to a central server, objects are tracked. Another research assumes that only the neghborng cameras are calbrated (Ca and Aggarwal, 1999). Objects are tracked by a sngle camera untl the system swtches to another camera for a better vew. Although, there are multple cameras n the system but objects are tracked on one camera at a tme. Bayesan networks were used to match features and establsh correspondences (Chang and Gong, 2001; Utsum et al., 2000). However, relyng on features matchng can cause problems as the features can be seen dfferently by dfferent cameras due to lghtng varatons. One of the feasble approaches that can be used to solve the consstent labelng s estmatng a homograph (Lee, 2000). By ths way, the scene s planar and a homograph s estmated between the planes n dfferent camera vews. Consstent labelng can be establshed wth usng feld of vew lnes (Khan and Shah, 2003). Ths method does not requre the cameras to be calbrated. Trackng Algorthm In Overlappng Feld Of Vews Vehcles trajectores may not always be tracked relably on one camera vew due to the errors resultng from the vehcle detecton by the performance of the background subtracton algorthm. The problem of mult-vew objects trackng has been addressedn many papers (Lu et al., 2013; Berclaz et al., 2009; Butt and Collns, 2013; Fleuret et al., 2011; Babae and Fathy, 2010; Babae and Fathy, 2011), but almost the survellance system s desgned centrally. In our survellance system the poston of cameras s shown n Fgure 1.The system sdecentralzed andeach of the cameras has four processng cores n dfferent levels (fgure 2). Frst, nput vdeo sequences are fed to detecton level of cameras. At the Decson level, control commands are ssued to classfy the detected vehcles based on extracted descrpton featuresprocess cores n three upper levels exchange the requste nformaton to track and recognze more accurately. Fgure1. Cameras poston of cooperatve system The prncple features of our scheme whch s summarzed as cooperatve dstrbuted vdeo survellance system are partcularly well-suted for low bandwdth; therefore the requrement processng s done locally and the method does not requre the pre-calbraton nto the scene and hence, can be used n traffc scenes where the system admnstrator may not have control over the actvtes takng place. If Cam = { cam1, cam2}be a set of two partally overlappng synchronzed cameras, v represent the vehcle observed n cam(n), thet (x (t), y (t)) s the resultng Pont trackng of v on locaton (x, y) at nstant (t) n cam(n). The trajectory of v s a set of all observatons, where represent the length of the trajectory: T = {T (x (0), y (0)), T (x (1), y (1)),, T (x (L), y (L))} 3659

3 Intl. Res. J. Appl. Basc. Sc. Vol., 4 (12), , 2013 Fgure2. Cooperatve Levels n proposed dstrbuted system In order to determne the mathematcal relatonshp between the observatons from two dfferent vews, we calbrate the two cameras usng fve control ponts as shown n fgure 3. In traffc survellance, an accurate camera calbraton smply means that the 2D mage coordnate can be properly predcted gven the 3D locaton of the vehcle. (Babae and Fathy, 2011). Fgure3. Fve Control Ponts n overlappng felds of vew The transformaton from the real world poston x w, y w,z w to the 3D coordnates x 3D,y 3D,z 3D s gven by a rotaton matrx and a translaton vector as: x 3D T 11 T 21 T 31 x w T 41 y 3D = T 12 T 22 T 32 y w + T 42 (1) Z 3D T 13 T 23 T 33 Z w T 43 The transformaton from 3D camera coordnates x 3D,y 3D,z 3D to the 2D coordnates (Q x,q y ) s obtaned usng perspectve projecton as: Q X = x 3D f, Q z y = y 3D f (2) 3D z 3D Usng the calbraton technque presented by Lebe et al., (2007) and Barros et al., (2009), we know the homogeneous transformaton T, focal length f and the mage center (T x, T y )for camera I and S, f and (S x, S y )for camera II. The mage poston (Q 1x, Q ),(Q 1y 2x, Q 2y )and world poston x w, y w,z w are calculated by usng the translaton equatons. Knowng the camera s calbraton, we are able to merge the object trackng from two dfferent vews nto a real world coordnates vew. The fnal step s to fuse a par ofcorrespondng trajectores n overlappng felds as shown n fgure 4. To fuse T 1 andt 2, where both trajectores are generated from the same vehcle n world coordnates, we use an adaptve weghtng method, 3660

4 Intl. Res. J. Appl. Basc. Sc. Vol., 4 (12), , 2013 (3) αt 1 (t) + βt 2 (t) R 1,2 T 1,2 = T 1 (t) R 1 T 2 (t) R 2 Where R 1,2 s the regon where observatons from both T 1,W and T 2,W are avalable at tme t. R 1 and R 2 are the regons where the observaton s avalable from ether T 1,W or T 2,W, respectvely. The weghts α and β are calculated by number of observatons for each trajectory: T 1 T 2 α =, β = (4) T 1 T 2 T 1 T 2 Subject toα + β=1 Fnally, to construct a complete trajectory across the entre path, we connect all partal trajectores that belong to same vehcle. T = T, (5) Fgure4. Unform trajectory extracton wth two overlappng felds of vew cameras The global trajectory of a vehcle s obtaned from connectng the two partal trajectores n world coordnates. The reconstructon of vehcles trajectores across cameras facltates the recognton of global behavors for large scale events n traffc montorng and vdeo survellance. Block dagram n fgure 5 llustrates the mplementaton of the proposed cooperatve protocol wth communcaton between cameras. Both cameras wll need nformaton from the other camera when a new vehcle appears n ts feld of vew or a vehcle tracker cannot be matched to ts target vehcle. Therefore, Request messages wth format llustrated n table 1 s created to be sent to other camera. Vehcle Tracker Frst Camera Requester Lost Vehcle New Vehcle Request Message Lost Label Request Message New Label Second Camera Repler Send the Closest Tracker Label Send the Coordnates of Label Reply Message Lost Label Reply Message New Label Frst Camera Requester Fnd Lost Label Update Vehcle Tracker Fnd New Label Fgure5. Communcaton protocol between cameras 3661

5 Intl. Res. J. Appl. Basc. Sc. Vol., 4 (12), , 2013 As t s shown n table 1, the format of reply messages ncludes the found label and the new coordnates. If an old vehcle V 1 s not matched to any new extracted vehcles, t may have dsappeared or t may be occluded by another vehcle. Thus, f V 1 dsappears n the new frame, we check f there s a segment for another vehcle n another camera whch has same features wth V 1 s segment. If the answer s postve, vehcle s marked as occluded vehcle. Table 1.Messages format n communcaton protocol Messages Format New Label Request Message Packet No. Current Coordnates (x,y) Temporary Label Lost Label Request Message Packet No. Current Coordnates (x,y) Lost Label New Label Reply Message Packet No. New Coordnates (x,y) Found Label Lost Label Reply Message Packet No. New Coordnates (x,y) Found Label Another case s occurred when a new vehcle V n s not matched to any tracked vehcles, t may be a new vehcle enterng the scene, the result of mproper segmentaton, or a vehcle that was prevously occluded and s no longer occluded. If a new vehcle V n s detected, check whether there s a prevous vehcle, whose segment overlaps wth ths new vehcle n both cameras. If ts occluson bt s set, then splt V 1. Now, there are two vehcles V 1 and V 2 n prevous frames, whch were occluded now. Then,the correspondng trajectores are updated and occluson bts reset. If V n was not prevously occluded, ths s the result of mproper segmentaton, and should be merged. Otherwse, V n s a new vehcle that just entered the traffc scene. Expermental Results In our proposed method, calbraton s very mportant for whch the transfer of relevant data between cameras s essental. When a vehcle s lost n one camera vew, ts coordnates s updated from other camera vew and ths necesstates vdeo streams to be temporally synchronzed for relevant data transfer. In experments, we measured the accuracy of the data transfer and data updates. Ths measurement s computed based on the number of correct updates over total requests. The number of tmes a new label request or lost label request s repled correctly and correspondng tracker s correctly updated present the number of correct updates. The system accuracy for dfferent synchronzaton rates s shown n Table 2. Vdeo 1 Vdeo 2 Table2. Trackng accuracy for new and lost vehcles requests Trackng Accuracy 60s 180s 300s New Lost New Lost New Lost Request Messages Tracker updated correctly Accuracy 100% 85.71% 95.34% 87.50% 95.65% 85.31% Request Messages Tracker updated correctly Accuracy 96.00% 100% 95.94% 93.10% 97.32% 94.33% When consderng extracted trajectores n global vew, we evaluate the clusterng effcency to detect the abnormal trajectores, n ths case T, True Postve Trajectores, are those that are correctly labeled abnormal, F, False Postve Trajectores, are those that are ncorrectly labeled abnormal and F, False Negatve Trajectores, are those that are ncorrectly labeled normal. Vehcles detecton and segmentaton n double vew and overlappng feld s shown n fgure 6.Thus we need to see how the clusterng has been performed n detectng the abnormal trajectores n the sequences. In table 3, the true postve and false postve values wth consderng the occluded trajectores n abovementoned sequences have been presented separately. Notceably, 31 occluded trajectores are n S 1 and 23 occluded trajectores are n S 2. Fgure6.Vehcles detecton and segmentaton n double vew and overlappng feld 3662

6 Intl. Res. J. Appl. Basc. Sc. Vol., 4 (12), , 2013 Table3. True Postve and False Postve results True Postve & False Postve S 1 Results ( number of occluson: 31) S 2 Results ( number of occluson: 23) seconds True Postve False Postve seconds True Postve False Postve 120s s s s s s s s s s s s s s 41 2 CONCLUSION Ths paper presented a dstrbuted mult-camera system for multple vehcles trackng n urban traffc survellance. In ths cooperatve survellance system, two dfferent processng unts are used to process each camera and to communcate wth each other drectly elmnatng the need for a central server and to provde scalablty n computaton and removng a sngle pont of falure. Instead of transferrng the extracted features to a central server, each camera performs ts own trackng, keepng ts own trajectores for each vehcle n the partally overlappng felds of vew.a novel communcaton protocol was desgned to coordnate multple trackng components across the dstrbuted survellance system. We tested the performance on real world datasets and found that the precson and the recall results are promsng. Fnally, wth computng trackng accuracy for dfferent requests, New and lost label messages, we acheved at least 95.4% accuracy for new label request and 85.31% accuracy for lost label request. ACKNOWLEGMENT Authors are grateful to the Islamc Azad Unversty, Scence and research Branch for support to carry out ths work. REFERENCES Babae P, Fathy M Mult-Camera Systems Evaluaton n Urban Traffc Survellance versus Tradtonal Sngle-Camera Systems for Vehcles Trackng. 3rd Int. Conf. on Comp. and Elect. Eng. (ICCEE 2010), vol.4, pp Babae P, Fathy M A Dstrbuted Cooperatve System forunsupervsed Vehcle s Trackng and Recognton n Urban Traffc Survellance. 3th Int. Cof. on Machne Learnng and Computng (ICMLC 2011), vol.4, pp ,. Barros B, Hrstova S, Woehler C, Kummert F, Hermes C D pose estmaton of vehcles usng a stereo camera. In IEEE, Intellgent Vehcles Symposum. Ben Shtrt H, Berclaz J, Fleuret F, Fua P Trackng Multple Objects under Global Appearance Constrants. In Proceedngs of the IEEE Internatonal Conference on Computer Vson (ICCV), pp Berclaz Fleuret F, Fua P Multple Object Trackng usng Flow Lnear Programmng. In Proceedngs of the 12th IEEE Internatonal Workshop on Performance Evaluaton of Trackng and Survellance (Wnter-PETS), pages 1-8. Butt A, Collns T Mult-target Trackng by Larangan Relaxaton to Mn-Cost NetworkFlow.Computer Vson and Pattern Recognton (CVPR), pp.1-8. Ca Q, Aggarwal JK Trackng human moton n structured envronments usng a dstrbuted camera system. IEEE Transacton on Pattern Analyss and Machne Intellgence (PAMI), vol.21.no.11, pp Chang TH, Gong S Trackng multple people wth a mult-camera system. Proceedngs of IEEE Workshop on Mult-Object Trackng, pp Kelly PH, Katkere A, Kuramura DY, Moezz S, Chatterjee S, Jan R.1995.Archtecture for multple perspectve nteractve vdeo Proceedngs of ACM Conference on Multmeda, pp Khan S, Shah M Consstent labelng of tracked objects n multple cameras wth overlappng felds of vew. IEEE Transactons on Pattern Analyss and Machne Intellgence (PAMI), pp Lee L, Romano R, Sten G Montorng actvtes from multple vdeo streams: Establshng a common coordnate frame. IEEE Transactons on Pattern Analyss and Machne Intellgence,pp Lebe B, Cornels N, Cornels K, Van L Dynamc 3D scene analyss from a movng vehcle. In Computer Vson and Pattern Recognton, CVPR. pp Lu J, Collns T, Lu Y Robust Autocalbraton for Survellance Camera Network. Applcatons of Computer Vson (WACV), pp.1-8. Mathew TV, Krshna Rao KV Introducton to Transportaton Engneerng.Chapter 39,Traffc Intersectons,NPTEL. Utsum A, Mor H, Ohya J, Yachda M Multple-camera-based human trackng usng non-synchronous observatons. Proceedngs of Asan Conference on Computer Vson, pp