Particle Filter Tracking Method base on Feature Fusion and Particle Swarm Optimized

For the presence of particles of degraded traditional particle filter tracking algorithm when resampling, Not a good solution to partial occlusion morph targets and target tracking problems, This paper introduces the multi-feature fusion based particle swarm optimized particle filter tracking method. Particle swarm optimization algorithm uses particle weights be updated with the current estimate of the difference between the size of the state and the state of each particle as evaluation criteria, prompting the true state of the particle sampling area to move, reduce particle degradation, improve the tracking performance of the particle filter tracking algorithm. For target deformation and occlusion, This article introduced the Normalized moment of inertia (NMI) feature, Will it with color features multiplicative fusion strategy fusion is used to describe the target characteristics. Through the experiments on several standard test video, experimental results show that the proposed method for dynamic background scene morph targets and partial occlusion target tracking with better accuracy and robustness.