Autonomous Sports Training from Visual Cues

Computer driven biometric analysis of athlete's movements have proven themselves as effective sports training tools. Most current systems rely on the use of retro-reflective markers or magnetic sensors to capture the motion of the athlete, so the biometric analysis can be performed. Video based training tools have also proved to be valuable instructional aids, however most require significant human interaction for analysis to be performed. This paper outlines an ongoing project focussed on capturing posture without the use of any markers or sensors, while still capturing enough information for an automated analysis to be performed. The approach taken to solving this problem is presented, as well as the current state of development of a an instructional aid for golfers.

[1]  Michael Isard,et al.  Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking , 2000, ECCV.

[2]  James W. Davis,et al.  Virtual PAT: A Virtual Personal Aerobics Trainer , 1998 .

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

[4]  Timothy F. Cootes,et al.  View-based active appearance models , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[5]  John MacCormick,et al.  Stochastic Algorithms for Visual Tracking , 2002, Distinguished Dissertations.

[6]  Richard Szeliski,et al.  Tracking with Kalman snakes , 1993 .

[7]  Michael Isard,et al.  A Smoothing Filter for CONDENSATION , 1998, ECCV.

[8]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[9]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).