| --- |
| version: 1.0.0 |
| license: cc-by-nc-4.0 |
| task_categories: |
| - object-detection |
| - video-classification |
| tags: |
| - sports |
| - soccer |
| - football |
| - referee-tracking |
| - person-detection |
| annotations_creators: |
| - human-verified |
| - machine-generated |
| pretty_name: Soccer Referee Tracking Dataset |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # Soccer Referee Tracking Dataset |
|
|
| <p align="center"> |
| <img src="previews/sample_hd_2018.jpg" width="48%" alt="Sample: 2018 HD with 3 referees" /> |
| <img src="previews/sample_sd_1996.jpg" width="48%" alt="Sample: 1996 SD with 2 referees" /> |
| </p> |
|
|
| A curated dataset for detecting and tracking **referees** in professional soccer broadcast footage. This dataset supports the development of models that can distinguish referees from players, staff, and other on-field personnel under varied broadcast conditions. |
|
|
| ## Dataset Description |
|
|
| This public sample consists of **1,450 frames** extracted from **10 video clips** of professional soccer broadcasts. The data is split into two categories based on referee visibility: |
|
|
| - **Visible**: Frames where at least one referee is clearly visible and annotated with a bounding box. |
| - **Not Visible**: Frames where no referee is visible in the frame (negative samples). |
|
|
| This is a representative subset of a larger internal dataset, selected to cover diverse match conditions (SD/HD, different teams, mined vs. segmented clips). |
|
|
| ### Statistics |
|
|
| | Category | Samples | Description | |
| |----------|---------|-------------| |
| | **Visible** | 802 | Frames with at least one referee bounding box | |
| | **Not Visible** | 648 | Frames with no visible referee (hard negatives) | |
| | **Total** | **1,450** | Total frames from 10 clips | |
|
|
| ### Source Data |
|
|
| - **Domain**: Professional Soccer Broadcasts |
| - **Resolution**: Varied |
| - **Annotation Style**: YOLO format (normalized xywh) |
| - **Labeling Method**: Active Learning Loop (COCO Pre-labeling -> Manual Verification) |
| - **Anonymization**: Source video names have been replaced with UUIDs. |
|
|
| ## Dataset Structure |
|
|
| ``` |
| infactory-ai/referee-tracking/ |
| ├── README.md |
| ├── metadata.csv |
| ├── dataset_info.json |
| └── data/ |
| ├── visible/ |
| │ ├── {uuid}_{frame}.jpg |
| │ └── {uuid}_{frame}.txt # YOLO label |
| └── not_visible/ |
| └── {uuid}_{frame}.jpg |
| ``` |
|
|
| ### Metadata Fields (`metadata.csv`) |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `file_path` | string | Relative path to the image file | |
| | `video_source` | string | UUID of the source video clip | |
| | `frame_index` | int | Frame number in the original clip | |
| | `visibility` | string | `visible` or `not_visible` | |
| | `bboxes_count` | int | Number of bounding boxes in the frame | |
|
|
| ## Usage |
|
|
| ### Loading with Hugging Face Datasets |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("infactory-ai/referee-tracking", data_dir="data") |
| |
| # Filter for visible frames |
| visible_frames = dataset.filter(lambda x: x["visibility"] == "visible") |
| ``` |
|
|
| ### Parsing Labels |
|
|
| Labels are in standard YOLO format: |
| `<class_id> <x_center> <y_center> <width> <height>` |
|
|
| * `class_id`: 0 (referee) |
| * Coordinates are normalized to [0, 1]. |
|
|
| ## Team |
|
|
| | Name | Role | |
| |------|------| |
| | **Valentino Constantinou** | Head of Infrastructure | |
| | **Dr. Mehdi Iranmanesh** | Applied AI Engineer | |
| | **John Kanalakis** | Chief Technology Officer | |
|
|
| ## License |
|
|
| This dataset is released under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). |
|
|
| **You are free to:** |
| - **Share** -- copy and redistribute the material in any medium or format |
| - **Adapt** -- remix, transform, and build upon the material |
|
|
| **Under the following terms:** |
| - **Attribution** -- You must give appropriate credit to Infactory, provide a link to the license, and indicate if changes were made. |
| - **Non-Commercial** -- You may not use the material for commercial purposes without a separate commercial license from Infactory. |
|
|
| **Commercial licensing:** For commercial use, contact [hello@infactory.ai](mailto:hello@infactory.ai). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{referee_tracking_2026, |
| title={Soccer Referee Tracking Dataset}, |
| author={Constantinou, Valentino and Iranmanesh, Mehdi and Kanalakis, John}, |
| year={2026}, |
| publisher={Infactory}, |
| url={https://huggingface.co/datasets/infactory-ai/referee-tracking} |
| } |
| ``` |
|
|
