Abstract

The problem of tracking a varying number of non-rigid ob- jects has two ma jor dificulties. First, the observation models and target distributions can be highly non-linear and non-Gaussian. Second, the presence of a large, varying number of ob jects creates complex inter- actions with overlap and ambiguities. To surmount these dificulties, we introduce a vision system that is capable of learning, detecting and track- ing the ob jects of interest. The system is demonstrated in the context of tracking hockey players using video sequences. Our approach combines the strengths of two successful algorithms: mixture particle filters and Adaboost. The mixture particle filter [17] is ideally suited to multi-target tracking as it assigns a mixture component to each player. The crucial design issues in mixture particle filters are the choice of the proposal distribution and the treatment of ob jects leaving and entering the scene. Here, we construct the proposal distribution using a mixture model that incorporates information from the dynamic models of each player and the detection hypotheses generated by Adaboost. The learned Adaboost pro- posal distribution allows us to quickly detect players entering the scene, while the filtering process enables us to keep track of the individual play- ers. The result of interleaving Adaboost with mixture particle filters is a simple, yet powerful and fully automatic multiple ob ject tracking system.