arXiv Computer Vision
Sep 25

Training-Free Hold-Usage Detection in Sport Climbing with Foundation Pose Models

The paper presents a training‑free method for detecting which holds a climber uses in sport climbing videos by leveraging a frozen foundation pose model (Sapiens) that provides fingertip and toe keypoints. Using a simple proximity test, mutual exclusion, and a temporal‑persistence rule, the approach achieves high F_1 scores (up to 90.2%) on the Way Up dataset without any climbing‑specific training, outperforming repurposed pose pipelines. The resulting automatic predictions enable accurate coaching statistics, such as climb time and pace, with Pearson correlations of 1.00 and 0.94 respectively.

By Abu Bakar, Abdullah Aftab, Amir Hamza
arXiv Computer Vision
Oct 23

V-JEPA 2 Meets AQA: Phase-Based Scoring and Reference-Based Feedback for Mountain Biking

The paper presents a system for assessing mountain‑bike jump quality using weak binary labels and candidate feedback. It processes 95 field‑recorded jump clips with frozen V‑JEPA 2 video features, pose and motion measurements, and a transformer to predict a binary score. The model achieves an AUROC of 0.768 and average precision of 0.812 in cross‑validation, but the results highlight limitations in rider‑independent scoring and reliable feedback.

By Santhiya Rajan, Savio Cardoz
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.

By Francisco Erramuspe Alvarez, Shobharani Polasa, Weihao Qu, Jay Wang, Ling Zheng
arXiv AI
Oct 31

Can Event Detectors Leave the Broadcast Camera? Evidence from Tennis

The paper introduces a new benchmark of 345 annotated tennis ball bounces and racket hits across eight video clips featuring diverse viewpoints, including broadcast, fixed elevated, phone, and court-level angles. It evaluates several event detection methods—supervised spotters, zero-shot vision-language models, and a parameter-free arbitration layer called RallyArb—showing that supervised models perform well on broadcast footage but poorly on non-broadcast views, while RallyArb maintains high bounce detection across all viewpoints but struggles with precise hit timing. The dataset, evaluation tools, and frozen predictions are publicly released to support further research.

By David Soeiro-Vuong, Andre-Louis Rochet