Assessment of deep learning pose estimates for sports collision tracking

Abstract

Injury assessment during sporting collisions requires estimation of the associated kinematics. While marker-based solutions are widely accepted as providing accurate and reliable measurements, the setup times are lengthy and it is not always possible to outfit athletes with restrictive equipment in sporting situations. A new generation of markerless motion capture based on deep learning techniques holds significant promise for enabling measurement of movement in the wild. The aim of the current work is to evaluate the performance of a popular deep learning model "out of the box" for human pose estimation on a staged rugby tackle dataset of ten staged tackle movements performed in a marker-based motion capture laboratory with a system of three high-speed video cameras. An analysis of the discrepancy between joint positions estimated by the marker-based and markerless systems shows that the deep learning approach performs acceptably well in most instances, although high errors exist during challenging intervals of heavy occlusion and self-occlusion. In total, 75.6% of joint position estimates are found to have a mean absolute error (MAE) of less than or equal to 25 mm, 17.8% have an MAE between 25 and 50 mm and 6.7% have an MAE greater than 50 mm. The mean per joint position error is 47 mm.

Computer Vision Technologies for Human Pose Estimation in Exercise: Accuracy and Practicality

“…Presently, Computer Vision technologies find applications across diverse spectra, encompassing the tracking of movements, both collective and individual, in team sports (Cioppa et al, 2020); the contemporaneous monitoring of athletes' performance (Citraro et al, 2020); body position tracking to prevent injury (Blythman et al, 2022); and the analysis of motion patterns during rehabilitative physical exercises as well as fitness activities (Rahmadani, Dewantara & Sari, 2022). Particular attention is merited for the domain encompassing the the human pose estimation during physical exercises (Andriluka, Pishchulin, Gehler & Schiele, 2014).…”