---
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}
}
```