SOURCE-LINKED INTELLIGENCE
MS-RFD: Multi-Signal Release Frame Detection in Hammer Throw from Reconstructed 3D Trajectories
Recent advances in artificial intelligence and computer vision are reshaping sports performance analysis by enabling automated detection, tracking, and performance analysis. In hammer throw, performance is strongly determined by the kinematic conditions at release, particularly release speed, release angle, and release height. However, identifying the release instant from video typically requires manual frame-by-frame inspection, which is subjective and cumbersome in real-world training scenarios. In this paper, we present a fully automatic multi-signal release frame detection (MS-RFD) method
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T07:38:59.000Z
First collected: 2026-09-20T08:01:03.945Z. This is not the publication date.