A novel approach for tracking high speed skaters in sports using a panning camera

This paper presents a computer vision system for tracking high-speed non-rigid skaters over a larger rink in short track speed skating competitions. The outputs of the tracking system are spatio-temporal trajectories of the skaters which can be further processed and analyzed by sports experts. To capture highly complex and dynamic scenes, the camera pans very fast, therefore, tracking amorphous skaters becomes a challenging task. We propose a new method for (1) automatically computing the transformation matrices to map each frame to the globally consistent model of the rink; (2) incorporating the hierarchical model based on the contextual knowledge and multiple cues into the unscented Kalman filter to improve the tracking performance when occlusions occur; (3) evaluating the precision of our practical system objectively. Experimental results show that the proposed algorithm is very efficient and effective on the video recorded in the World Short Track Speed Skating Championships.