Multi signal method detects hammer throw release frames automatically
MS-RFD: Multi-Signal Release Frame Detection in Hammer Throw from Reconstructed 3D Trajectories
Computer Vision and Pattern Recognition
Summary
Determining the exact moment a hammer is released in hammer throw is important but usually requires careful and slow manual video review. The authors developed a computer method that uses four different movement clues from 3D reconstructed hammer paths to automatically find the release frame. This approach combines speed patterns, turning speed changes, distance from the center, and straightness of the hammer’s flight after release. Their tests showed that speed and distance clues were most helpful, with the other signals giving smaller improvements.
What this means in practice
- •For sports performance analysts: Automatically identify hammer release instant from videos for objective training feedback without manual sorting.
- •For sports technology developers: Integrate multi-signal detection into sports analytics software to improve hammer throw performance monitoring.$Commercial implications: Enables commercial sports analysis products to automatically detect critical throwing moments, enhancing user value.
Authors
Ahmed Endris Hasen, Nikolaos Passalis, Tomi Vanttinen, Jenni Raitoharju
Abstract
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 for hammer throw using reconstructed 3D hammer trajectories. The proposed method integrates four complementary kinematic signals: speed dynamics, angular velocity transition, radial distance relative to the rotation center, and post-release trajectory linearity. These signals are fused to score and verify candidate release frames. MS-RFD is evaluated through the throwing-distance estimation error obtained from the release parameters estimated at the detected frame. An ablation study analyzes the contribution of each signal and compares alternative candidate selection strategies. The results show that speed dynamics and radial expansion provide the strongest signals for release frame detection, while angular velocity and post-release linearity provide smaller refinements.