Shannon Entropy-Based Adaptive Fusion Particle Filter for Visual Tracking

Shannon entropy is effective uncertainty measurement criterion for stochastic system. In this paper, adaptive fusion particle filter is proposed for visual tracking by introduced Shannon entropy in particle filter framework. Firstly, the particle filter, which is considered as the process of particles assimilating negative entropy to reduce uncertainty, is surveyed from viewpoint of information theory. Secondly, maximum negative entropy criterion is proposed to select tracking feature form features pool online. At last, color histogram and edge orientation histogram features are utilized in experiments, tracking results show that the proposed algorithm is a robust and accuracy tracking algorithm.