Human Pose Estimation by Exploiting Spatial and Temporal Constraints in Body-Part Configurations

We present an algorithm for estimating a sequence of articulated upper-body human pose in unconstrained videos. Most previous work often fails to locate forearms in those video scenes suffering from illumination varieties, background clutter, camera shake, or occlusion. In order to deal with such intractable cases, we propose a novel algorithm for addressing the problem of certain body parts localization. The proposed approach can be roughly divided into two steps: first, a spatial model is designed to capture the high-order relationship between adjacent parts and meanwhile to generate a set of configurations in each frame under the temporal context constraint; second, a competitive method is presented to select the best body parts among diverse pose configurations. In this paper, the proposed algorithm focuses on the unconstrained video scenes and improves the detection precision of certain body parts with high degree of freedom. Moreover, the proposed algorithm can be well applied to a very challenging dataset named Movies. Experimental results show that the proposed algorithm can dramatically improve performance compared with those related algorithms on two benchmark datasets (MPII and SHPED datasets) and on our Movies dataset.