The reviewed record of science sign in
Pith

arxiv: 2508.01802 · v1 · pith:LUR46CPY · submitted 2025-08-03 · cs.CV

SoccerTrack v2: A Full-Pitch Multi-View Soccer Dataset for Game State Reconstruction

Reviewed by Pithpith:LUR46CPYopen to challenge →

classification cs.CV
keywords soccertracksocceractionanalyticsdatasetdatasetsgamelabels
0
0 comments X
read the original 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Towards Athlete Fatigue Assessment from Association Football Videos

    cs.CV 2026-04 unverdicted novelty 4.0

    Monocular broadcast videos can produce acceleration-speed profiles compatible with fatigue analysis in football, though sensitive to trajectory noise and calibration errors.