Correcting Pose Estimation with Implicit Occlusion Detection and Rectification

Recently, articulated pose estimation methods based on the pictorial structure framework have received much attention in computer vision. However, the performance of these approaches has been limited due to the presence of self-occlusion. This paper deals with the problem of handling self-occlusion in the pictorial structure framework. We propose an exemplar-based framework for implicit occlusion detection and rectification. Our framework can be applied as a general post-processing plug-in following any pose estimation approach to rectify errors due to self-occlusion and to improve the accuracy. The proposed framework outperforms a state-of-the-art pictorial structure approach for human pose estimation on the HumanEva dataset