3D human motion tracking based on a progressive particle filter

Human body tracking has received increasing attention in recent years due to its broad applicability. Among these tracking algorithms, the particle filter is considered an effective approach for human motion tracking. However, it suffers from the degeneracy problem and considerable computational burden. This paper presents a novel 3D model-based tracking algorithm called the progressive particle filter to decrease the computational cost in high degrees of freedom by employing hierarchical searching. In the proposed approach, likelihood measure functions involving four different features are presented to enhance the performance of model fitting. Moreover, embedded mean shift trackers are adopted to increase accuracy by moving each particle toward the location with the highest probability of posture through the estimated mean shift vector. Experimental results demonstrate that the progressive particle filter requires lower computational cost and delivers higher accuracy than the standard particle filter.