Dynamic Kernel-Based Progressive Particle Filter for 3D Human Motion Tracking

This paper presents a novel tracking algorithm, the dynamic kernel-based progressive particle filter (DKPPF), for markless 3D human body tracking. An articulated human body contains considerable degrees of freedom to be estimated. The proposed algorithm aims to reduce the computational complexity and improve the accuracy. The DKPPF decomposes the high dimensional parameter space into three low dimensional spaces and hierarchically searches the posture coefficients. Moreover, it applies multiple predictions and a mean shift tracker to estimate the human posture iteratively. A dynamic kernel model is proposed to automatically adjust the kernel bandwidth of mean shift trackers according to the probability distribution of the posture states. The kernel model is capable of improving the accuracy of the tracking result. The experimental examples show that the proposed approach can effectively improve the accuracy and expedite the computation.