Percorrer por autor "Ferreira, Bruno Filipe Baptista"
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- Volleyball performance statistics based on video analysis and deep learningPublication . Ferreira, Bruno Filipe Baptista; Torres, José; Soares, ChristopheThis dissertation presents the development and evaluation of a semi-automatic video-based system for extracting volleyball performance statistics using computer vision and deep learning techniques. Performance analysis plays a critical role in modern volleyball, supporting coaches and analysts in understanding match dynamics, technical execution, and tactical behaviour. However, the collection of such statistics is often performed manually or semimanually, making the process time-consuming, difficult to scale, and prone to inconsistency. To address these limitations, this work proposes a modular analysis pipeline centred on ball detection, tracking, scoreboard reading, court calibration, rally management, and event interpretation. The system employs a YOLObased model for volleyball detection, combined with temporal tracking strategies that exploit spatial continuity, motion constraints, foreground validation, and outlier rejection. Manual initialisation is still required for court calibration and scoreboard region selection, but the subsequent analysis stage operates automatically over the selected video segment. The work integrates these components into a unified prototype for volleyball match analysis. The system produces structured outputs describing rallies and event categories such as attacks, blocks, balls out, balls on the net, aces, and errors. The implemented pipeline was evaluated on two recorded volleyball sets from different venues. The system achieved a manual-review agreement rate of 81.82% on the first evaluated set and 72.50% on the second, corresponding to an overall agreement rate of 77.38% across 84 reviewed points. The evaluation shows that the proposed approach can extract useful volleyball event information from fixed-camera match footage, while also highlighting important limitations. The main sources of disagreement were related to fast rallies, trajectory ambiguity near the net and court boundaries, visual interference from additional balls, and venue-specific background complexity. The prototype therefore offers a concrete starting point for future work on semi-automatic volleyball performance analysis, particularly through improved ball tracking, more robust scoreboard recognition, and broader evaluation across additional venues.