Players tracking and ball detection for an automatic tennis video annotation

Abstract

This paper describes our algorithms for players tracking and ball detection for an automatic broadcast tennis video annotation. The system detects and tracks the players using a robust non-parametric procedure for estimating density gradients called the mean shift algorithm. The basic mean shift tracking algorithm assumes that the target object has to separate sufficiently from background, but this assumption is not always true especially when tracking is carried out in dynamic backgrounds such as in sport videos. To cope with this problem, in our proposed system, we embrace the motion segmentation and use the 8×8×8 color histogram to be feature distribution for mean shift tennis players tracking. In order to determine the players' actions precisely, the system also detect and track ball positions using frame differencing as well as applying some correlation techniques to eliminate false detections. Based on both players' motion patterns and ball positions, the system can precisely classify the players' action into backhand ground stroke and forehand ground stroke. Videos of broadcast tennis games downloaded from the Internet have been tested. The results show our system is able to precisely classify the player's actions with 83.7% precision and 82% recall rates.