Probabilistic Tracking of the Soccer Ball

This paper proposes an algorithm for tracking the ball in a soccer video sequence. Two major issues in ball tracking are 1) the image portion of the ball in a frame is very small, having blurred white color, and 2) the interaction with players causes overlapping or occlusion and makes it almost impossible to detect the ball area in a frame or consecutive frames. The first is solved by accumulating the image measurements in time after removing the players’ blobs. The resultant image plays a role of proposal density for generating random particles in particle filtering. The second problem makes the ball invisible for time periods. Our tracker then finds adjacent players, marks them as potential ball holders, and pursues them until a new accumulated measurement sufficient for the ball tracking comes out. The experiment shows a good performance on a pretty long soccer match sequence in spite of the ball being frequently occluded by players.

[1]  Boon-Lock Yeo,et al.  Analysis And Presentation Of Soccer Highlights From Digital Video , 1995 .

[2]  Hideo Saito,et al.  Tracking Soccer Player Using Multiple Views , 2002, MVA.

[3]  Yoshiaki Shirai,et al.  Tracking players and a ball in video image sequence and estimating camera parameters for 3D interpretation of soccer games , 2002, Object recognition supported by user interaction for service robots.

[4]  H.-W. Ok.  Multiple Soccer Players Tracking by Condensation with Occlusion Alarm Probability , 2002 .

[5]  Aaron F. Bobick,et al.  Closed-world tracking , 1995, Proceedings of IEEE International Conference on Computer Vision.

[6]  A. Doucet,et al.  On sequential sampling Monte Carlo sampling methods for Bayesian filtering , 2000 .

[7]  Michael Isard,et al.  Active Contours , 2000, Springer London.

[8]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[9]  Alessandro Bevilacqua.  A Novel Background Initialization Method in Visual Surveillance , 2002, MVA.

[10]  Yongduek Seo,et al.  Where Are the Ball and Players? Soccer Game Analysis with Color Based Tracking and Image Mosaick , 1997, ICIAP.

[11]  L. Davis,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002, Proc. IEEE.

[12]  Shaogang Gong,et al.  Object Tracking Using Adaptive Color Mixture Models , 1998, ACCV.

[13]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[14]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[15]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[16]  Isaac Cohen,et al.  Soccer Player Tracking across Uncalibrated Camera Streams , 2004 .

[17]  Lillian Lee,et al.  Similarity-Based Approaches to Natural Language Processing , 1997, ArXiv.

[18]  Young-Kyu Yang,et al.  A Soccer Image Sequence Mosaicking and Analysis Method Using Line and Advertisement Board Detection , 2002 .

[19]  Ichiro Ide,et al.  An object detection method for describing soccer games from video , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.