Accurate Human Pose Estimation by Aggregating Multiple Pose Hypotheses Using Modified Kernel Density Approximation

Abstract

This letter proposes an accurate human pose estimation method that uses a modified kernel density approximation (m-KDA) to multiple pose hypotheses. Existing methods show poor human pose estimation because of cluttered background or self-occlusion by the human. To improve the pose estimation accuracy, we propose to use m-KDA to aggregate multiple pose estimation results. First, we use the flexible mixture-of-parts model (FMM) to estimate the human poses then use the top-M scores to choose the good pose hypotheses. Second, we aggregate the top-M pose hypotheses with the m-KDA, in which each kernel density function is modified by each pose's score value and each pose's compatibility function that represents how far each pose hypothesis is departed from the nominal value of top-M pose hypotheses. Third, we determine the optimal pose configuration by repeating the above m-KDA computation, starting from the root part (head) to the leaf parts (hands and feet), sequentially. In pose estimation experiments on two benchmark datasets (PARSE and LSP), the proposed method achieved 1.5-4.0% improvement in the percentage of correct localized parts (PCP) over the state-of-the-art methods.