Automatic player detection, labeling and tracking in broadcast soccer video

In this paper, we present a method to perform automatic multiple player detection, unsupervised labeling and efficient tracking in broadcast soccer videos. Player detection is to determine the players’ positions and scales. It is achieved by combining the ability of dominant color based background subtraction and a boosting detector with Haar features. We then collect hundreds of player samples with the player detector, and learn codebook based player appearance model by unsupervised clustering algorithm. A player can be labeled as one of four types: two teams, referee or outlier. The learning capability enables the method to be generalized well to different videos without any manually initialization. Based on detection and labeling, we perform multiple player tracking with Markov chain Monte Carlo (MCMC) data association. Some data driven dynamics are proposed to improve the Markov chain’s efficiency, such as label and motion consistent and track length. The testing results on FIFA World Cup 2006 videos demonstrate that our method can reach high detection and labeling precision, and reliably tracking in cases of scenes such as player occlusion, moderate camera motion and pose variation.