发表机构
University of Jyväskylä; Aristotle University of Thessaloniki; Finnish Institute of High Performance Sport KIHU（于韦斯屈莱大学; 亚里士多德大学; 芬兰高性能体育研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出MS-RFD方法，利用重建3D轨迹融合速度、角速度、径向距离和轨迹线性度四种信号，自动检测链球释放帧，实验表明速度动态和径向扩展信号最有效。
AI 中文摘要
人工智能和计算机视觉的最新进展正在通过实现自动检测、跟踪和性能分析来重塑体育表现分析。在链球项目中，成绩在很大程度上取决于释放时的运动学条件，特别是释放速度、释放角度和释放高度。然而，从视频中识别释放瞬间通常需要人工逐帧检查，这在现实训练场景中既主观又繁琐。本文提出了一种全自动的多信号释放帧检测（MS-RFD）方法，利用重建的3D链球轨迹进行检测。该方法整合了四个互补的运动学信号：速度动态、角速度转变、相对于旋转中心的径向距离以及释放后轨迹的线性度。这些信号被融合以对候选释放帧进行评分和验证。MS-RFD通过从检测帧处估计的释放参数获得的投掷距离估计误差进行评估。一项消融研究分析了每个信号的贡献，并比较了替代的候选选择策略。结果表明，速度动态和径向扩展为释放帧检测提供了最强的信号，而角速度和释放后线性度提供了较小的细化。
英文摘要
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.
Comments6 pages, 4 figures
