Human Pose Estimation Based Biomechanical Feature Extraction for Long Jumps

Biomechanical features describing movements and poses of athletes have been proposed by experts to help study athletic performances, but the traditional way of measuring those features are high-cost, time-consuming and intrusive. In this paper, we propose a deep learning-based method that can estimate athletic biomechanical features from typical broadcast competition videos, i.e. single-camera-shot moving videos. This method involves state-of-the-art human pose estimation models and a biomechanical analysis to reconstruct the trajectory. We then leverage the reconstructed trajectory to estimate the target features. To evaluate the method, we gathered a dataset from the long jump World Championships of 2017 and 2018, comprising 22 expert-proposed long-jump biomechanical features about the trajectories, taking-off and landing characteristics. Our experiments show the effectiveness of the pipeline in automatically estimating the biomechanical features. By analysing the results, we identify the challenges towards high-accuracy athletes' feature estimations from monocular broadcast competition videos. Code is available at https://github.com/QGAN2019/Long_Jump_Feature_Estimation.