Visible Joint Classification and Temporal Segment Matching based 3D Pose Refinement for Volleyball Receive Analysis

Abstract

Receive action pose analysis is very meaningful in volleyball games for training and strategy. Because receive action is lack high-quality labeled data sets and with problems like occlusion, body overlap, and abnormal pose. Conventional work fails to obtain high accurate pose results. This paper proposes visible joint refinement and receive action template matching for volleyball receive action analysis. Firstly, the visible joint is using pixel color feature and the potential constraint features between space and joints to classify the visible joint and refine the error visible joint. Secondly, the template is to realize the pose segment refinement at 3D level by matching. It is based on a multi-view system for a real volleyball competition scene. The dataset video is from the Game of 2014 Japan Inter High School of Men Volleyball. The experiment result achieves 95.33 %, 96.92 %, and 98.43 % success rate at the 30 mm, 50 mm, and 70 mm error ranges.