Articulated Gaussian kernel correlation for human pose estimation

In this paper, we address the problem of human pose estimation through a novel articulated Gaussian kernel correlation function which is applied to human pose tracking from a single depth sensor. We first derive a unified Gaussian kernel correlation that can generalize the previous Sum-of-Gaussians (SoG)-based methods for the similarity measure between a template and the observation. Furthermore, we develop an articulated Gaussian kernel correlation by embedding a tree-structured skeleton model, which enables us to estimate the full-body pose parameters. Also, the new kernel correlation framework can easily penalize undesired body intersection which is more natural than the clamping function in previous methods. Our algorithm is general, simple yet effective and can achieve real-time performance. The experimental results on a public depth dataset are promising and competitive when compared with state-of-the-art algorithms.