3D pose reconstruction with multi-perspective and spatial confidence point group for jump analysis in figure skating

Abstract

Driven by recent computer vision applications, recovering 3D pose in the field of figure skating has become increasingly important. However, conventional works have suffered because of getting 3D information based on the corresponding 2D information directly or leaving the specificity of sports out of consideration. Issues such as restriction from self-occlusion, abnormal pose, limitation of venue and so on will result in poor results. Motivated by these problems, this paper proposes a multitask architecture based on a calibrated multi-camera system to facilitate jointly 3D jump pose of figure skater in the presence of the 2D Part Confidence Map. The proposals consist of three key components: Temporal smoothness and likelihood distribution based discrete probability points selection; Multi-perspective and combinations unification based large-scale venue 3D reconstruction; Spatial confidence point group and multiple constraints based human skeleton estimation. This work can be applied to 3D animated display and video motion capture of figure skating competition. The accuracy rate on the test sequences is 82.32% in body level and 92.96% in joint level.