Robust tracking of soccer players based on data fusion

Presents a technique for integrating multiple visual features for tracking moving objects. Our proposed method consists of observation (pattern-matching) units and prediction units, which form a ladder structure. The major feature of our proposed method is that each of the observation units with different pattern matching algorithms is executed step-by-step to innovate the state vector considering the reliability of the observation. The fusion of multiple observations makes the tracks robust to occlusion and to deformation. Experiments with soccer sequences are shown to validate the technique's robustness. Its applications to broadcasting services are also briefly discussed.