Pose Estimation Based on Pose Cluster and Candidates Recombination

Abstract

Pose estimation is a task with wide application prospects in computer vision, which remains a challenging problem. In this paper, a novel pose estimation algorithm is proposed on the basis of pose clustering and body-part candidates recombination. Different from most previous methods with a single pictorial structure (PS) model, we generate mixture PS models based on clusters of the poses to achieve more faithful appearances and spatial relations estimation within each cluster. In addition, to address the problems of individual body-part false detection and double-counting, we extract some of the best estimation results in the optimal clustered model as the candidates of body parts and recombine them by solving a constrained maximization problem. Experiments on a public challenging data set show that our method is more accurate than the state-of-the-art algorithms and performs effectively in tackling the double-counting phenomena.