Monocular broadcast videos can produce acceleration-speed profiles compatible with fatigue analysis in football, though sensitive to trajectory noise and calibration errors.
SoccerTrack v2: A full-pitch multi- view soccer dataset for game state reconstruction.arXiv, abs/2508.01802
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
SoccerTrack v2 is a new public dataset for advancing multi-object tracking (MOT), game state reconstruction (GSR), and ball action spotting (BAS) in soccer analytics. Unlike prior datasets that use broadcast views or limited scenarios, SoccerTrack v2 provides 10 full-length, panoramic 4K recordings of university-level matches, captured with BePro cameras for complete player visibility. Each video is annotated with GSR labels (2D pitch coordinates, jersey-based player IDs, roles, teams) and BAS labels for 12 action classes (e.g., Pass, Drive, Shot). This technical report outlines the datasets structure, collection pipeline, and annotation process. SoccerTrack v2 is designed to advance research in computer vision and soccer analytics, enabling new benchmarks and practical applications in tactical analysis and automated tools.
fields
cs.CV 2years
2026 2representative citing papers
The SoccerNet 2026 Challenges benchmarked 427 teams across five soccer video understanding tasks, with leading submissions improving over baselines on all tasks.
citing papers explorer
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Towards Athlete Fatigue Assessment from Association Football Videos
Monocular broadcast videos can produce acceleration-speed profiles compatible with fatigue analysis in football, though sensitive to trajectory noise and calibration errors.
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SoccerNet 2026 Challenges Results
The SoccerNet 2026 Challenges benchmarked 427 teams across five soccer video understanding tasks, with leading submissions improving over baselines on all tasks.