⚽ SoccerTrack v2

TL;DR

• 10 full matches (~900 minutes) of 4K panoramic full-pitch footage covering entire playing field
• Per-frame GSR annotations: 2D pitch coordinates, track IDs, jersey numbers, roles, and team assignments
• Ball Action Spotting: 12 action classes (Pass, Drive, Shot, Header, Cross, etc.) with global timestamps
• Three benchmark tasks: Multi-Object Tracking (MOT), Game State Reconstruction (GSR), Ball Action Spotting (BAS)
• Open access: Available on GitHub and Google Drive for reproducible research

Dataset Visuals

Panoramic 4K Coverage

Daytime Match - Full Pitch Coverage

Night Match - Stadium Conditions

GSR Annotations Example

Demo Video

What is SoccerTrack v2?

SoccerTrack v2 addresses critical gaps in existing soccer datasets by providing full-pitch panoramic coverage with comprehensive per-frame annotations. Unlike broadcast-view datasets limited by occlusions and partial field coverage, our dataset captures the entire pitch using panoramic 4K cameras.

The dataset features 10 university-level amateur matches recorded with BePro camera systems, providing approximately 900 minutes of gameplay. Each frame is annotated with detailed game state information including player positions in 2D pitch coordinates, persistent track IDs, jersey numbers, player roles (player/goalkeeper/referee), and team assignments.

Beyond tracking, SoccerTrack v2 includes Ball Action Spotting annotations covering 12 action classes aligned to the video timeline, enabling comprehensive tactical analysis and event detection research.

Dataset Contents

Matches & Videos

• 10 university-level amateur matches
• Approximately 900 minutes of gameplay
• 4K panoramic MP4 videos with full-pitch coverage
• Camera setup: BePro Cerberus (2 matches) + 3-camera panoramic systems (8 matches)

GSR (Game State Reconstruction) Annotations

Per-frame annotations including:

• 2D pitch coordinates (meters) for all players
• Unique track IDs persistent throughout the match
• Player roles: player, goalkeeper, referee, other
• Team assignments: left, right, or null
• Jersey numbers: 0–99 or null

BAS (Ball Action Spotting) Annotations

12 action classes with global timestamps:

Tasks & Benchmarks

Game State Reconstruction (GSR) →

GSR: Panoramic view → 2D pitch coordinates with player roles

Generate 2D pitch minimaps from panoramic video. Reconstruct complete game state including all player positions, roles, and team assignments for tactical analysis. This task evaluates the ability to accurately map players from video frames to standardized pitch coordinates while maintaining identity and role information.

Inspired by SoccerNet GSR Challenge

Ball Action Spotting (BAS) →

BAS: Temporal action detection with 12 ball event classes

Detect and classify 12 types of ball actions from video. Event detection task aligned with global timestamps for comprehensive match analysis. Actions include Pass, Drive, Shot, Header, High Pass, Out, Cross, Throw In, Ball Player Block, Player Successful Tackle, Free Kick, and Goal.

Inspired by SoccerNet BAS Challenge

Multi-Object Tracking (SoccerTrack Challenge) →

MOT: Persistent player tracking with bounding boxes and IDs

Full-pitch player tracking with persistent IDs across long sequences. Evaluate tracking performance, ID maintenance, and re-identification in complex game scenarios. A subset of matches with bounding box annotations is featured in the SoccerTrack Challenge 2025.

Data Format & Folder Structure

The dataset is organized as follows:

production/
├── videos/          # Panoramic 4K video files (1 MP4 per match)
│   ├── 117093.mp4
│   └── ...
├── gsr/             # Game State Reconstruction annotations
│   ├── 117093/
│   │   └── 117093.json
│   └── ...
├── bas/             # Ball Action Spotting annotations
│   ├── 117093/
│   │   └── 117093.json
│   └── ...
├── mot/             # Multi-Object Tracking annotations (MOTChallenge format)
│   ├── 117093/
│   │   ├── gt/
│   │   │   └── gt.txt
│   │   └── seqinfo.ini
│   └── ...
└── raw/             # Original calibration data and source files
    ├── 117093/
    │   ├── 117093_keypoints.json
    │   ├── 117093_mapx.npy
    │   ├── 117093_mapy.npy
    │   ├── 117093_12_class_events.json
    │   ├── 117093_tracker_box_data.xml
    │   └── ...
    └── ...

GSR Annotation Fields

• frame: Frame number
• time: Timestamp in video
• player_id: Unique track ID (persistent throughout match)
• x, y: 2D pitch coordinates in meters
• role: player / goalkeeper / referee / other
• team_side: left / right / null
• jersey_number: 0–99 / null

BAS Annotation Fields

• time: Global timestamp aligned to video timeline
• event_class: One of 12 action classes
• team: Team performing the action
• player_id: Track ID (when available)

Download Dataset

Getting Started

1. Download the Dataset

Download the dataset files from Google Drive.

2. Clone the Repository

git clone https://github.com/AtomScott/SoccerTrack-v2.git
cd SoccerTrack-v2
pip install -r requirements-dev.txt

3. Visualize Tracking Data

python -m src.main command=plot-coordinates-on-video \
  plot_coordinates_on_video.match_id=117093

See the scripts/ directory for data preprocessing pipelines, feature extraction, model training configurations, and evaluation benchmarks. All experiments are fully reproducible.

License & Terms of Use

SoccerTrack v2 is released under the MIT License. The dataset has been collected with approval from the university ethics board, and all data has been de-identified.

• No player names are included; identification is jersey-number based only
• All matches feature university-level amateur players with informed consent
• Free for both academic research and commercial use

Please see the LICENSE file for full details.

Citation

If you use this dataset in your research, please cite:

@article{soccertrack_v2_2025,
  title={SoccerTrack v2: A Full-Pitch Multi-View Soccer Dataset for Game State Reconstruction},
  author={Scott, Atom and others},
  journal={arXiv preprint arXiv:2508.01802},
  year={2025},
  url={https://arxiv.org/abs/2508.01802}
}

Acknowledgements

This work was supported by JST SPRING (Grant Number JPMJSP2108) and JSPS KAKENHI. We thank all participating teams and universities for their cooperation in data collection.

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