Human body posture refinement by nonparametric belief propagation

Accurate human body posture refinement from single or multiple images is essential in many applications. Two main causes of difficulty to solve the refinement problem are high degree freedom of human body and self-occlusion. One of the most recent algorithms is nonparametric belief propagation (NBP) that solves the problem in a lower dimensional state space. However, it is difficult to handle self-occlusion. This paper presents an NBP-based algorithm that can refine body posture even in self-occlusion case, which has been shown by experimental results. The experimental results also show that our algorithm can accurately refine body posture even if the initial posture has large difference from the true posture.