Boosted Interactively Distributed Particle Filter for automatic multi-object tracking

In this paper, we propose a boosted interactively distributed particle filter (BIDPF) to address the problem of automatic multi-object tracking in the application of player tracking in broadcast soccer video. The interactively distributed particle filter technique (IDPF) is adopted to handle the mutual occlusions among targets. The proposal distribution using a mixture model that incorporates information from the dynamic model and the boosting detection is introduced into the IDPF framework. The boosting proposal distribution quickly detects targets, while the IDPF process keeps the identity of targets during mutual occlusions. Moreover, the foreground observation is extracted by using the color model of the playfield to speed up the boosting detection and reduce false alarms. The foreground is also used to develop a data-driven potential model to improve the IDPF performance. We test the proposed approach on several video sequences and the results demonstrate that our system is able to track a variable number of objects in a dynamic scene and correctly maintain their identities regardless of camera motion and frequent mutual occlusions.