2D Human Pose Estimation Using Multi-level Dynamic Model

A human pose estimation algorithm with a multi-level dynamic model for monocular videos is presented. Firstly, a multi-level dynamic model of human pose is constructed to decompose human entire pose into articulated pose parts, and approach optimal human pose candidates by optimizing pose parts candidates. This model solves the ambiguity problem caused by the entire pose estimation method. Secondly, an algorithm for calculating the pose consistency between the adjacent video frames is proposed by constructing virtual poses. This method can make use of the continuity of appearance features and motion features between the adjacent frames to improve the estimation accuracy. Thirdly, particle swarm optimization method is utilized to search for the best pose parts candidates with a small amount of candidates, and then the achieved pose parts are recomposed into the optimal human entire poses. The efficiency of the proposed method is tested and experimentally compared with several related state-of-the-art methods on challenging video sequences, which shows significant improvements.