Robust human pose estimation from corrupted images with partial occlusions and noise pollutions

Abstract

Robust human pose estimation from the given visual observations has attracted many attentions in the past two decades. However, this problem is still challenging due to the situation that observations are often corrupted with partial occlusions or noise pollutions or both in real-world applications. In this paper, we propose to estimate human pose by using robust silhouette matching in original rectangle-coordinate space. In addition, human action model is employed to determinate reasonable matching results. Experimental results on robustness sequence of Weizman dataset reveal that our proposed approach can estimate human pose robustly and reasonably when pose observations are corrupted with partial occlusions or noise pollutions.