Adaptive multi-feature tracking in particle swarm optimization based particle filter framework

This paper proposes a particle swarm optimization (PSO) based particle filter (PF) tracking framework, the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage, and simultaneously incorporates the newest observations into the proposal distribution in the update stage. In the proposed approach, likelihood measure functions involving multiple features are presented to enhance the performance of model fitting. Furthermore, the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process. There are three main contributions. Firstly, the PSO algorithm is fused into the PF framework, which can efficiently alleviate the particles degeneracy phenomenon. Secondly, an effective convergence criterion for the PSO algorithm is explored, which can avoid particles getting stuck in local minima and maintain a greater particle diversity. Finally, a multi-feature weight self-adjusting strategy is proposed, which can significantly improve the tracking robustness and accuracy. Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.