Biomechanical-phase based Temporal Segmentation in Sports Videos: a Demonstration on Javelin-Throw

• URL: http://arxiv.org/abs/2509.24606v1
• Date: Mon, 29 Sep 2025 11:11:46 GMT
• Title: Biomechanical-phase based Temporal Segmentation in Sports Videos: a Demonstration on Javelin-Throw
• Abstract summary: Traditional sports analytics techniques rely on manual annotation or laboratory-based instrumentation.<n>We present a novel unsupervised framework for such a contextually aware temporal segmentation.<n>Our approach achieves 71.02% mean average precision (mAP) and 74.61% F1-score on test data, substantially higher than competing baselines.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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