Tracking sports players over a large playing area is a challenging problem. The players move quickly, and have large variations in their silhouettes. This paper presents a framework for multi-object tracking, using a CONDENSATION based approach. Each player being tracked is independently fitted to a model, and the sampling probability for the group of samples is calculated as a function of the fitness score of each player. This function rewards consistently good scores, but punishes a group of some very good and some very bad fitness scores. Ground plane information is used throughout, and the predictive stage of the algorithm is improved to incorporate estimates of position from Kalman filters. This helps group the estimated positions of each player, and to aid in tracking through occlusions.
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