Particle filter based on Particle Swarm Optimization resampling for vision tracking

Particle filter is a powerful tool for vision tracking based on Sequential Monte Carlo framework. The core of particle filter in vision tracking is how to allocate particles to a high posterior area. Particle Swarm Optimization (PSO) is applied to find high likelihood area in this paper. PSO algorithm can search the sample area around the last time object position depending on current observation. So, it can distribute the particles in high likelihood area even though the dynamic model of the object cannot be obtained. Our algorithm does not distribute the particles based on the weight of the particles last time like the sampling-importance resampling (SIR). SIR usually allures particles distributed in wrong likelihood area particularly tracking in cluttered scene. Since that some particles have larger weight maybe illusive. We first find the sample area by PSO algorithm, then we distribute the particles based on two different base points in order to achieve diversity and convergence. Experimental results in several real-tracking scenarios demonstrate that our algorithm surpasses the standard particle filter on both robustness and accuracy.