Accurate and Efficient 3D Human Pose Estimation Algorithm Using Single Depth Images for Pose Analysis in Golf

Human pose analysis has been known to be an effective means to evaluate athlete's performance. Marker-less 3D human pose estimation is one of the most practical methods to acquire human pose but lacks sufficient accuracy required to achieve precise performance analysis for sports. In this paper, we propose a human pose estimation algorithm that utilizes multiple types of random forests to enhance results for sports analysis. Random regression forest voting to localize joints of the athlete's anatomy is followed by random verification forests that evaluate and optimize the votes to improve the accuracy of clustering that determine the final position of anatomic joints. Experiential results show that the proposed algorithm enhances not only accuracy, but also efficiency of human pose estimation. We also conduct the field study to investigate feasibility of the algorithm for sports applications with developed golf swing analyzing system.