Integration of a skier-specific keypoint detection model in a hybrid 3D motion capture pipeline

作者：Michael Zwölfer, M. Mössner, Helge Rhodin, W. Nachbauer, Dieter Heinrich · 发表于：Current Issues in Sport Science (CISS) · 年份：2024 · DOI：10.36950/2024.4ciss013 · 被引用次数：2

Introduction & Purpose Alpine skiing, like many outdoor sports, presents significant challenges for motion capture due to its large capture volumes, high athlete speeds, variable environmental conditions, and occlusions, e.g., due to snow spray. While traditional marker-based motion capture systems offer highest precision in the lab, they are usually unsuitable for outdoor settings. Sensor-based methods, such as inertial measurement units, however, may suffer from inaccuracies due to sensor noise and drift, while they only provide relative segment positions (Fasel et al., 2018). Therefore, recent studies in alpine skiing preferably used video-based systems (Heinrich et al., 2023; Spörri, 2016). These methods rely on multi-camera setups that require synchronization and camera calibration. However, the extensive manual digitization required for both keypoints and reference points introduces a substantial workload in post-processing, particularly when cameras must pan, tilt, and zoom to cover large capture volumes (Spörri, 2016). Recent advancements in computer vision have unveiled great potential for human motion capture, especially in automating much of the manual work required for video-based systems (Fang et al., 2017; Redmon et al., 2016; Zwölfer et al., 2023a). We therefore developed a novel, hybrid 3D motion capture approach that automates the detection of reference points using a reference point detection algorithm and the digitization of keypoints using a skier-specific...