Video-based Training Registration for Swimmers

During the last decade, performance improvements in top sports have been increasingly driven by technological innovations. This paper discusses the application of video analysis for training registration in swimming. In current practice, coaches have limited means to evaluate objectively and quantitatively how a training session was carried out. We propose the use of a video-based registration system in order to help the coach in acquiring such information. The system uses multiple cameras to cover the swimming pool. By using a simple background modeling and blob tracking method swimmers are tracked and their lap times are estimated. The main limitation of the system is the failure to detect swimmers at the pool ends while they are resting or underwater. This can lead to the necessity to perform manual interactions to associate laps to swimmers and a systematic underestimation of the lap times of typically 1.5 seconds. Our results can be used to formulate training guidelines that can help overcome some limitations of the system so that, with little or no manual effort, the system could be used in practice to do quantitative measurements. The information on the actual training performance of the swimmers could then be compared with the training schedule made beforehand and used to further optimize the training program.

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

[2]  Ming Xu,et al.  Tracking football players with multiple cameras , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[3]  David C. Hogg,et al.  An Adaptive Eigenshape Model , 1995, BMVC.

[4]  David J. Fleet,et al.  Monocular 3-D Tracking of the Golf Swing , 2005, CVPR.

[5]  Fatih Porikli,et al.  Performance Evaluation of Object Detection and Tracking Systems , 2006 .

[6]  David S. Doermann,et al.  Tools and techniques for video performance evaluation , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[7]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[8]  Nandan Parameswaran,et al.  Survey of Sports Video Analysis: Research Issues and Applications , 2003, VIP.

[9]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[10]  Wei-Yun Yau,et al.  Robust human detection within a highly dynamic aquatic environment in real time , 2006, IEEE Transactions on Image Processing.

[11]  Arnold W. M. Smeulders,et al.  Tracking Aspects of the Foreground against the Background , 2004, ECCV.

[12]  I. Haritaoglu,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002 .

[13]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.

[14]  Dariu Gavrila,et al.  Real-time object detection for "smart" vehicles , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  E. R. Davies,et al.  Machine vision - theory, algorithms, practicalities , 2004 .