3D Human Pose Estimation for Martial Arts Analysis through Graph Convolutional Networks

Abstract

This paper presents a human pose estimation method for martial arts video analysis using a Semantic Graph Convolutional Network (SemGCN) instead of an ordinary convolutional neural network (CNN). The inputs for the model are videos from the Human3.6M dataset, in addition to the ones from Martial Arts, Dancing and Sports (MADS) dataset. A data unification process is described so that MADS joints can be adapted to the Human3.6M base setting. The performance of the model when only uses Human3.6M for training is compared to training with both Human3.6M and MADS datasets, resulting in a lower mean per-joint position error (MPJPE) for the latter. Finally, performance indicators such as the vertical position of the center of mass, balance and stability, are calculated for the MADS sequences in order to provide insights regarding martial arts execution.