Self-occlusion robust 3D human pose tracking from monocular image sequence

Abstract

Pose tracking technique has great potential for many applications such as marker-free human motion capture system, Human Computer Interactions (HCI), and video surveillance. Though many methods are introduced during last decades, self-occlusion - one body part is occluded by another one - is still considered one of the most difficult problems for 3D human pose tracking. In this paper, we propose a self-occlusion state estimation method. A MRF (Markov Random Field) is used to model the occlusion state which represents the pairwise depth order between two human body parts. A novel estimation method is proposed to infer a body pose and an occlusion state separately. HumanEva dataset is used for testing the proposed method. In order to evaluate and quantify how often the occlusion state changes, we label the ground truth of occlusion state.