Skip to Main Content
Using particle filter to track human movement, a key problem is how to draw samples in high-dimensional state space. In this paper, we present a novel framework of particle filtering, namely Hierarchical Genetic Particle Filter (HGPF), to improve the efficiency of samples by a hierarchical evolutionary detection. As a result, we can obtain reasonably distributed samples thus translating into reliable tracking performance. Finally, we apply the technique to 2D articulated human movement tracking. Result demonstrates the effectiveness of HGPF in solving the tracking problem like self-occlusion and cluttered background.