Vollytics: A Computer Vision System for Single-Camera Event Spotting and Rally Reconstruction in Beach Volleyball

Beach volleyball video analysis is challenging because technical actions are short, the ball is small and fast, player occlusions are frequent, and broadcast footage contains camera cuts. This paper presents Vollytics a modular single-camera framework for technical-action detection, event spotting, rally reconstruction, and score inference from broadcast and fixed-camera videos. The system combines a You Only Look Once (YOLO)-based detector with court geometry estimation, geometry-aware filtering, lightweight player–ball association, and volleyball-specific temporal reasoning to transform frame-level visual observations into a structured representation of match progression. We curate 23,651 annotated frames containing player, ball, and technical-action bounding boxes, together with a video-based dataset comprising 231 action events and 44 rallies. Frame-level technical-action detection achieves a Macro Action F1-score of 0.572, while temporal consolidation yields a Macro F1-score of 0.851 for event spotting under an absolute temporal tolerance of 2 s. At rally level, Vollytics reconstructs 43 of 44 annotated rallies, achieving an F1-score of 0.966. On three complete broadcast matches, score inference produces an absolute point-count error of 8 over 215 official points (3.7%). The detector runs at 30.8 FPS on the tested GPU, supporting faster-than-real-time analysis of 25 FPS video. These results provide an initial validation of integrating frame-level visual perception with spatial and temporal domain reasoning for automatic beach-volleyball match analysis. Compared with previous single-camera beach-volleyball approaches, Vollytics provides a more complete framework in terms of functional scope and evaluation breadth, extending isolated tracking or interaction recognition to quantitatively evaluated event spotting, rally reconstruction, and progressive score inference.