3D Human Motion Tracking Based on Sequential Monte Carlo Method

Characterized by its high dimension and multi-modal, the 3D human motion tracking has been a stubborn problem in computer vision for many years. This paper introduces a new sequential Monte Carlo (SMC) method, based on annealed particle filtering, to solve the problem. Firstly, the new method adopts the state space decomposition in conjunction with simulated annealing to improve the annealing efficiency in a comparably lower dimension; next, the PERM sampling after every annealing, instead of standard resampling, is used to compensate the error between the observation model and the true target distribution. At the end of this paper, a simulated experiment is given to show that our improved SMC-based method is capable of tracking 3D articulated human.