无需人工标注，自动识别标枪投掷的运动阶段
Biomechanical-phase based Temporal Segmentation in Sports Videos: a Demonstration on Javelin-Throw
- 用结构化最优传输增强注意力时空图卷积网络
- 在新数据集上达到71.02% mAP和74.61% F1-score
- 适合体育动作分析与自动化运动评估研究者
精准分析运动员动作是体育数据分析的核心，尤其在标枪投掷等项目中，细微的生物力学阶段直接影响成绩。传统方法依赖人工标注或实验室设备，成本高且难以扩展。本文针对精英级标枪投掷，提出一种新型无监督框架，通过引入结构化最优传输（SOT）来增强注意力时空图卷积网络（ASTGCN），实现无需昂贵标注的运动阶段精准分割。实验表明，该方法在测试集上取得71.02%的平均精度（mAP）和74.61%的F1分数，显著优于现有无监督基线。同时，我们发布了包含211段专业标枪投掷视频、帧级标注的新数据集，涵盖准备步、推进、投掷和恢复四个关键生物力学阶段。
原文摘要 · Abstract (English)
Precise analysis of athletic motion is central to sports analytics, particularly in disciplines where nuanced biomechanical phases directly impact performance outcomes. Traditional analytics techniques rely on manual annotation or laboratory-based instrumentation, which are time-consuming, costly, and lack scalability. Automatic extraction of relevant kinetic variables requires a robust and contextually appropriate temporal segmentation. Considering the specific case of elite javelin-throw, we present a novel unsupervised framework for such a contextually aware segmentation, which applies the structured optimal transport (SOT) concept to augment the well-known Attention-based Spatio-Temporal Graph Convolutional Network (ASTGCN). This enables the identification of motion phase transitions without requiring expensive manual labeling. Extensive experiments demonstrate that our approach outperforms state-of-the-art unsupervised methods, achieving 71.02% mean average precision (mAP) and 74.61% F1-score on test data, substantially higher than competing baselines. We also release a new dataset of 211 manually annotated professional javelin-throw videos with frame-level annotations, covering key biomechanical phases: approach steps, drive, throw, and recovery.
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