Hierarchical Annealed Particle Swarm Optimization for Articulated Object Tracking

In this paper, we propose a novel algorithm for articulated object tracking, based on a hierarchical search and particle swarm optimization. Our approach aims to reduce the complexity induced by the high dimensional state space in articulated object tracking by decomposing the search space into subspaces and then using particle swarms to optimize over these subspaces hierarchically. Moreover, the intelligent search strategy proposed in [20] is integrated into each optimization step to provide a robust tracking algorithm under noisy observation conditions. Our quantitative and qualitative analysis both on synthetic and real video sequences show the efficiency of the proposed approach compared to other existing competitive tracking methods.