Problem
Research questions and friction points this paper is trying to address.
release frame detection
hammer throw
3D trajectories
kinematic conditions
performance analysis
MS-RFD: Multi-Signal Release Frame Detection in Hammer Throw from Reconstructed 3D Trajectories
🤖 AI Summary
本文提出了一种基于重建3D轨迹的多信号释放帧检测方法MS-RFD，用于自动检测链球投掷中的释放瞬间，通过整合速度、角速度、径向距离和释放后轨迹线性度四个运动信号来确定最佳释放帧。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-Signal Release Frame Detection
3D Trajectories
Kinematic Signals
Hammer Throw
Automated Analysis
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