Tracking High Speed Skater by Using Multiple Model

Transcription

1 Vol. 2, No. 26 Tracing High Speed Sater by Uing Multiple Model Guojun Liu & Xianglong Tang School of Computer Science & Engineering Harbin Intitute of Technology Harbin 5000, China The reearch i financed by the National Natural Science Foundation of China Grant No ). Abtract Color-baed tracing i a very popular technique for object tracing. However, under varying environmental condition in video equence, the exiting tracing method tend to be unreliable for tracing high peed object. It i poible under uch a complex environment that the target may diappear totally or partially due to occluion. To over thi problem, a novel approach of multiple-color model i propoed in thi paper. Different color extracted from the ater head and body are traced with Kalman filter, repectively. A confidence meaure repreenting the reliability of each color tracing reult i preented to guide poible re-detection for continuou tracing. Experimental reult how that the propoed approach i very efficient and effective in tracing high-peed ater in real hort trac equence. Keyword: Multiple model tracing, Kalman filter, Occluion. Introduction The mot common method to trac object i to detect them uing bacground ubtraction of an etablihed correpondence frame by frame Wren et al., 997; Lou et al., 2002). Depite it popularity, it can only be applied to imagery acquired by tationary camera. An alternative approach to bacground ubtraction i uing imple geometric model to tranform the object from frame to frame. Comaniciu et al. Comaniciu et al., 2003) ue the mean-hift approach to compute the tranlation of a circular region. The object appearance i modeled by weighed hitogram. Tracing of the complete object can be achieved by employing the active contour, which were introduced by Ka et al Ka et al., 988). In Manouri, 2002), Nonrigid object motion can be modeled in term of combining optical flow with active contour, Manouri ue a probabilitic form of the brightne contraint, where the color i defined by a Gauian ditribution. Optical flow derived from the brightne contraint i computed. However, the brightne contraint require very mall variation in the intenitie and it i not uitable for image with high dynamic intenity range. Tracing with multiple cue fuion become more and more popular. In Ozyildiz et al., 2002), an autobinomial Gibb Marov random field i ued for modeling the texture and a 2D Gauian ditribution i ued for modeling the color. Thi allow a probabilitic fuion of the texture and color cue. Similarly, in ilmaz et al., 2004), viual feature color, texture) and the object hape are conidered. Color and texture model are generated for the object and the bacground region. A hape prior i ued for recovering occluded object part during the occluion. There are many computer viion ytem for many port domain. Pingali et al. Pingali et al, 998) develop a real time tracing technology for enhancing the broadcat of tenni matche from tationary camera. Recently, an et al. an et al, 2006) propoe a data aociation algorithm to trac a tenni ball in low-quantity tenni video equence. Per et al. ue two tationary camera mounted directly above the court and propoe a new approach for modeling the radial image ditortion more accurately. The tracing algorithm combined with color feature and the template i exploited to trac the player, uing color feature i to avoid a drift caued by the template tracing. Their ytem are applied to many port domain including handball Per et al, 2002) and baetball Jug et al, 2003). But there are two limitation in their wor: firt, the camera mut be placed above the playing court, which i a rigorou condition for regular league or championhip matche. Second, how to handle occluion in tracing proce eem not to be olved. The goal of our tudy i to trac high-peed nonrigid ater under a complex environment, where bacground dratically change and ucceive occluion often occur and the color of imilar clothing are very cloe, etc. Many above method uing ideal model or ome contraint are not uitable. In thi paper, a novel approach for tracing high-peed ater i propoed. Color feature are firtly extracted from

2 February, 2009 ater head and body. Then, Kalman filter i utilized to etimate the earch region and to evaluate each color, repectively. Afterward, thee evaluation are integrated to determine which part of the object need to re-detect. The detection reult i regarded a the initial data of tracing module. To enhance the robutne of tracing, a confidence meaure repreenting the reliability of each color tracing reult i preented to guide poible re-detection for continuou tracing. Moreover, the lot tolerance of object i introduced in order to avoid topping tracing exceptionally due to totally occluion. The approach i robut and can perform real-time ater detection and tracing. 2. Propoed Approach Our computer viion ytem aim to automatically trac the movement of ater on a large-cale complex and dynamic rin. Our goal i to exploit it not only in daily training but alo in competition. We ued a ingle panning camera, which wa mounted at the top auditorium of the tadium a cloe a poible to the center in order to reduce the projection error. Due to little texture information on the rin, unlie Intille & Bobic, 994; Ouma, 2003), zooming wa abandoned, it can mae recording the high-peed target more difficult and enlarge the error of len ditortion. Though the camera center move by a mall amount, due to an offet from the camera' optical center, the approximation of pure rotation i indeed ufficient uch a proven in Hayman & Murray, 2003). 2. The Framewor of Our Algorithm The flow chart of the propoed algorithm hown in Figure conit of three major proceing module: ) Object detection baed on color. 2) Kalman filter prediction. 3) Object re-detection determined by confidence meaure. 2.2 Color Space Selection An important apect of any color-baed tracing ytem i to chooe a color pace that i relatively invariant to minor illuminant change. The two mot popular color pace robut to minor illuminant change are HSV and normalized RGB. Terrillon & Aamatu, 2000) and Bradi, 988) proved that normalized RGB color model i more enitive to lighting change ince the aturation influenced by lighting i not eparated out of that model. Hence the bet color pace i HSV which ha been applied to many color tracing problem Bradi, 988; Sigal et al., 2004). HSV pace eparate out hue and aturation from intenity, and we create color model by 2D hitogram from the hue and aturation channel in HSV pace. 2.3 Kalman filter prediction The Kalman filter i one of the mot popular etimation technique in motion prediction becaue it provide an optimal method for linear dynamic ytem with white Gauian noie. In general, the Kalman filter decribe a ytem with a ytem tate model and a meaurement model a in Eq. ) and 2): S A S w ) M H S v The ytem tate S at the time i linearly aociated with the tate at the time, and there i alo a linear relationhip between the meaurement M and the ytem tate. The random variable and S w v repreent the tate and meaurement noie, repectively. A i the tate tranition matrix that relate the tate at frame to the tate at frame +, and H i called the obervation matrix that relate the tate to the meaurement. In our cae, the ytem tate S conit of the following component: x : x coordinate of Center y : y coordinate of Center v : horizontal velocity of Center x S v : vertical velocity of Center 3) y w : width of bounding box h : height of bounding box : area of T arget The tate tranition matrix A, the obervation matrix H and the meaurement M are given by: 2) 27

3 Vol. 2, No A H M [ x', y', v ', ', '] T x vy 6) 2.4 Object Confidence Meaure It i frequently the cae in our tracing problem that an actual meaurement on a target i not reliable due to occluion and complex environment. When an actual meaurement i heavily corrupted, the meaurement error accumulate through the feedbac cycle and the reliable etimation can not be obtained, which reult in tracing failure. Therefore, we hould get an optimal detection reult a poible a we can. A confidence meaure CM) repreenting the reliability of each color tracing reult i given by the following formula: 4) 5) PH ) 0 PB ) 0 0 PH ) 0 PB ) CM PH ) PB ) Perror ) PH ) PB ) 0 0 H PH ) H B PB ) B 0 P error) P H, H ) ) P B, B ) 7) 8) 9) 0) v v where H and B repreent the detected area of head and body at time, repectively. H and B repreent the velocity of head and body at time, repectively. and are weighted factor, Th i a prior threhold of velocity. Conequently, the unreliable part determined by CM will be re-detected by the reliable part according to the correlation of initial model. 3. Experimental Reult In hort trac ating, the ater are very cloe and compete with other furiouly and overtaing i allowed all the time. A a reult, occluion happen frequently and ucceively, which i a real challenge to the robutne of tracing 28 0 PH, H ) 0 PB, B ) H H Th H H Th B B Th B B Th ) 2)

4 February, 2009 ytem. The experiment demontrate that the performance of the propoed approach i very good. In Figure 2, the competitor ating through the curve i exactly traced depite ucceive occluion and ize variation. Figure 3 how good tracing reult under a complex environment, where the color of advertiement baffle-board i imilar with that of ater clothing body). The experiment reult alo demontrate the robutne of our method. Once one of multiple color feature i traced exactly or not corrupt) even none of them i traced within limit frame, our tracing ytem can till wor continuouly. The unreliable color feature determined by confidence meaure CM) i re-detected according to the correlation of the initial model, which improve the tracing accuracy in every frame to avoid accumulated error. The trajectory of a ater in an individual race of 500 meter i illutrated in Fig. 4, different line denote the trajectory of the ater when he/he ate along different loop. More information and tatitic of the competition are available uch a the trajectory and velocity, which can be further proceed and analyzed by the port expert. Our approach ave more computational time than that in Ozyildiz et al., 2002). The program ha been implemented by uing Viual C++ on a PIV 3.0GHz machine and it tae only 28m to proce averagely on a true color image with reolution and can achieve real-time proceing. 4. Concluion A novel approach for tracing high-peed ater i propoed. Color feature are firtly extracted from ater head and body. The combination of them i efficient and effective for tracing ater under a complex environment or ucceive occluion. A confidence meaure repreenting the reliability of each color tracing reult i preented to guide poible re-detection for continuou tracing. Moreover, the lot tolerance of object i introduced in order to avoid topping tracing exceptionally due to totally occluion. The approach i robut and can achieve real-time ater detection and tracing. It will fail under ome pecial condition uch a all color feature loe ucceively or the light i too dar o a to the color feature in HSV pace i untable and not exactly detected due to non-removable ingularity Cheng et al., 200). It will be invetigated in the future. Reference A. Manouri. 2002). Region Tracing via Level Set PDE Without Motion Computation. IEEE Tran. Pattern Analyi and Machine Intelligence, vol. 24, no. 7, pp A. ilmaz, Xin Li, & M. Shah. 2004). Contour-Baed Object Tracing with Occluion Handling in Video Acquired Uing Mobile Camera. IEEE Tran. Pattern Analyi and Machine Intelligence, vol. 26, no., pp C.R. Wren, A. Azarbayejani, T. Darrell, & A.P. Pentland. 997). Pfinder: Real-Time Tracing of the Human Body. IEEE Tran. Pattern Analyi and Machine Intelligence, vol. 9, no. 7, pp D. Comaniciu, V. Rameh, & P. Meer. 2003). Kernel-Baed Object Tracing. IEEE Tran. Pattern Analyi and Machine Intelligence, vol. 25, no. 5, pp E. Ozyildiz, N.Krahntover, & R. Sharma. 2002). Adaptive texture and color egmentation for tracing moving object. Pattern Recognition, vol. 35, no.0, pp G.R. Bradi. 998). Computer Viion Face Tracing for Ue in a Perceptual Uer Interface. Intel Technology Journal. H.D. Cheng, X.H. Jiang,.Sun, & J.L.Wang. 200). Color image egmentation: advance and propect. Pattern Recognition, vol. 34, pp Hayman, E. & Murray, D. W ). The Effect of Tranlational Mialignment when Self-Calibrating Rotating and Zooming Camera. IEEE Tranaction on Pattern Analyi and Machine Intelligence, vol. 25, no. 8, pp Intille, S.S. & Bobic, A.F. 994). Tracing Uing a Local Cloed-World Aumption: Tracing in the Football Domain. MIT Media Lab Perceptual Computing Group Technical Report 296. J.C. Terrillon, & S.Aamatu. 2000). Comparative Performance of Different Chrominance pace for Color egmentation and Detection of Human Face in Complex Scene Image. Proc. Viion Interface, pp Jianguang Lou, Hao ang, Weiming Hu, & TieNiu Tan. 2002). An Illumination Invariant Change Detection Algorithm. The 5th Aian Conference on Computer Viion. Jug, M., Per, J., Dezman, B. & Kovacic, S ). Trajectory Baed Aement of Coordinated Human Activity. Proceeding of Third International Conference ICVS 2003, pp L. Sigal, S. Sclaroff, & V. Athito. 2004). Sin Color-Baed Video Segmentation under Time-Varying Illumination. IEEE Tran. Pattern Analyi and Machine Intelligence, vol. 26, no. 7, pp M. Ka, A. Witin, & D. Terzopoulo. 988). Snae: Active Contour Model. Int l J. Computer Viion, vol., no. 4, 29

5 Vol. 2, No. pp Ouma, K ). Automatic Acquiition of Motion Trajectorie: Tracing Hocey Player. The Univerity of Britih Columbia. Per, J., Bon, M., Kovacic, S., Sibila, M. & Dezman, B. 2002). Obervation and Analyi of Large-cale Human Motion. Human Movement Science, vol. 2, no.2, pp Pingali, G.S., Jean,. & Carlborn, I.. 998). Real Time Tracing for Enhanced Tenni Broadcat. International Conference on Computer Viion and Pattern Recognition, pp an, F., Kotin, A., Chritma, W. & Kittler, J ). A Novel Data Aociation Algorithm for Object Tracing in Clutter with Application to Tenni Video Analyi. International Conference on Computer Viion and Pattern Recognition, Vol., pp Image Sequence N N End N Figure. Flow chart of the propoed algorithm 30

6 February, 2009 Frame 320 Frame 325 Frame 330 Frame 335 Frame 340 Frame 345 Frame 355 Frame 365 Figure 2. Tracing reult of ucceive occluion Frame 375 Frame 376 Frame 377 Frame 378 Frame 379 Frame 380 Frame 38 Figure 3. Tracing reult under a complex environment Frame 382 Figure 4. The trajectory of a ater in an individual race of 500 meter 3