Enhanced Human Parsing with Multiple Feature Fusion and Augmented Pose Model

We address the problem of human pose estimation, which is a very challenging problem due to view angle variance, noise and occlusions. In this paper, we propose a novel human parsing method which can estimate diverse human poses from real world images. We merge the parallel lines feature and uniform LBP feature, thereby the new feature contains both shape and texture information, which can be used by discriminative body part detectors. The standard tree model is augmented by using virtual nodes in order to describe the correlations between originally unconnected nodes, which enhances the robustness of the traditional kinematic tree model. We test our method in a sports image dataset, and the experimental results demonstrate the advantages of the merged feature as well as the augmented pose model in real applications.