Multi-Technology Correction Based 3D Human Pose Estimation for Jump Analysis in Figure Skating

Jump analysis in figure skating is important. Recovering the 3D pose of a figure skater has become increasingly important. However, issues such as restrictions from an athlete’s clothing, self-occlusion, abnormal pose and so on will result in poor results. This paper proposes a multi-technology correction framework to obtain a 3D human pose. The framework consists of three key components: temporal information-based mutational point correction, multi-perspective-based reconstructed point selection and trajectory smoothness-based inaccurate point correction. Firstly, temporal information is used to correct the mutational points at the 2D level. Secondly, a multi-perspective is used to select the correct spatial points at the 3D level. Thirdly, trajectory smoothness is used to correct inaccuracies at the 3D level. This work will serve the purpose of displaying the 3D animated pose of a figure skater. The quality grade of the result rate on the test sequences is 87.25%.