Distributed Particle Filtering for Multiocular Soccer-Ball Tracking

This paper proposes a distributed state estimation architecture for multi-sensor fusion. The system consists of networked subsystems that cooperatively estimate the state of a common target from their own observations. Each subsystem is equipped with a self-contained particle filter that can operate in stand-alone as well as in network mode with a particle exchange function. We applied this flexible architecture to 3D soccer-ball tracking by modeling the imaging processes related to the centroid, size, and motion-blur of a target, and by modeling the dynamics with ballistic motion, bounce, and rolling. To evaluate the precision and robustness of the system, we conducted experiments using multiocular images of a professional soccer match.