arXiv AI
A 3D Pose-Based Ensemble Framework for Cricket Shot Classification and Automated Biomechanical Analysis
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arXiv Machine Learning
Aug 27
Multimodal Injury Risk Prediction in Tennis
The paper introduces PART, a multimodal predictive framework for tennis that combines physiological, training, sleep, questionnaire, jump, and video data from nine collegiate players to assess overall wellness, injury risk, physical capability, and playing style. Using machine learning and deep learning, PART provides holistic athlete assessments and forecasts specific injury risks to body areas such as elbows and knees. Evaluation shows strong performance in predicting wellness and injury risk, with potential benefits for recreational players who often injure themselves due to poor technique.
arXiv Computer Vision
Aug 21
PL-NBA: A Possession-level Universal Basketball Video Dataset Supporting Multiple Visual Understanding Tasks
arXiv:2608. 19646v1 Announce Type: new Abstract: Visual understanding in sports has emerged as a hot topic in computer vision in recent years.
arXiv Computer Vision
Sep 23
CricRAG: Retrieval Augmented Vision-Language Models for Personalized Cricket Coaching
arXiv:2609.26056v1 Announce Type: new Abstract: Vision-Language Models (VLMs) offer promising capabilities for automated sports coaching but face a fundamental limitation: they implicitly compare aga...
arXiv Computer Vision
Sep 10
TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos
arXiv:2605.01234v2 Announce Type: replace Abstract: We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides $140+$ hours of reconstructed singles and doubles gameplay from mon...
arXiv Computer Vision
3d ago
BMASH: Ball-Motion-Aware Soccer Header Spotting
arXiv:2609.39300v1 Announce Type: new Abstract: Recent advances in computer vision have made broadcast sports videos increasingly useful for event analysis, performance assessment, and player-safety...
arXiv AI
Jun 10
Integrated Real-Time Motion Tracking and AI Analysis for Athletic Performance Optimization
arXiv:2606. 09842v1 Announce Type: cross Abstract: Applying Human Pose Estimation (HPE) in real world environments remains a challenging task, this paper explores and surveys real time HPE approaches and their limitations in sports analysis for individuals, alongside developing a practical lightweight prototype for real world testing and usage.