Automatic Player Detection, Labeling and Tracking in Broadcast Soccer Video

Automatic player detection, labeling and tracking in broadcast soccer video are significant while quite challenging tasks. In this paper, we present a solution to perform automatic multiple player detection, unsupervised labeling and efficient tracking. Players’ position and scale are determined by a boosting based detector. Players’ appearance models are unsupervised learned from hundreds of samples automatically collected by detection. Thereafter, these models can be utilized for player labeling (Team A, Team B and Referee). Player tracking is achieved by Markov Chain Monte Carlo (MCMC) data association. Some data driven dynamics are proposed to improve the Markov chain’s efficiency. The testing results on FIFA World Cup 2006 video demonstrate that our method can reach high detection and labeling precision, and reliably tracking in cases of scenes such as multiple player occlusion, moderate camera motion and pose variation.