Badatya, Bikash KumarBikash KumarBadatyaBaghel, VipulVipulBaghelAmin, JyotirmoyJyotirmoyAminHegde, RaviRaviHegde2026-03-052026-03-052025-01-01[9798331598044]10.1109/STAR66750.2025.112647772-s2.0-105030335186https://repository.iitgn.ac.in/handle/IITG2025/34760Precise 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 datasubstantially 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.falseGraph ConvolutionOptimal Transport TheoryRepresentation LearningSkeleton-based Action LocalizationSports AnalyticsConference Paper19-2420250Conference PaperConference Paper