README.md

---
version: 1.0.0
license: cc-by-nc-4.0
task_categories:
  - image-classification
  - video-classification
language:
  - en
tags:
  - camera-detection
  - shot-scale
  - camera-angle
  - cinematography
  - broadcast
  - sports
  - soccer
  - computer-vision
  - infactory
annotations_creators:
  - machine-generated
  - expert-generated
pretty_name: Soccer Camera Detection Dataset
size_categories:
  - 1K<n<10K
---

# Soccer Camera Detection Dataset

A curated dataset for classifying camera characteristics in broadcast soccer video, annotated with **9-class shot scale** (CineScale-aligned) and **5-class camera angle** (CineScale2-aligned).

### Shot Scale Examples

<p align="center">
  <img src="previews/shot_scale_examples.jpg" width="100%" alt="Shot Scale: MCU, LS, ELS" />
</p>
<p align="center">
  <em>Left:</em> <strong>MCU</strong> Medium Close-Up — head & shoulders &nbsp;|&nbsp;
  <em>Center:</em> <strong>LS</strong> Long Shot — full body &nbsp;|&nbsp;
  <em>Right:</em> <strong>ELS</strong> Extreme Long Shot — wide pitch view
</p>

### Camera Angle Examples

<p align="center">
  <img src="previews/camera_angle_examples.jpg" width="100%" alt="Camera Angle: HIGH, NEU, LOW" />
</p>
<p align="center">
  <em>Left:</em> <strong>HIGH</strong> High Angle — elevated broadcast camera &nbsp;|&nbsp;
  <em>Center:</em> <strong>NEU</strong> Neutral — pitch-level eye height &nbsp;|&nbsp;
  <em>Right:</em> <strong>LOW</strong> Low Angle — camera below subject
</p>

## Dataset Description

This public sample consists of **650 frames** extracted from **10 video clips** of professional soccer broadcasts spanning 2013–2025. Each frame is annotated across two orthogonal dimensions:

- **Shot Scale** — how much of the subject is visible (9 classes from Extreme Close-Up to Extreme Long Shot)
- **Camera Angle** — the vertical orientation of the camera relative to the subject (5 classes from Overhead to Low Angle)

This is a representative sample of a larger dataset available for licensing. The full dataset covers hundreds of matches across multiple seasons and broadcast conditions — suitable for model training, fine-tuning, and research. Contact [hello@infactory.ai](mailto:hello@infactory.ai) for licensing details.

### Shot Scale Distribution

| Class | Frames | % |
|-------|--------|---|
| MLS | 167 | 25.7% |
| ELS | 159 | 24.5% |
| LS | 118 | 18.2% |
| MS | 85 | 13.1% |
| MCU | 50 | 7.7% |
| FS | 43 | 6.6% |
| IS | 14 | 2.2% |
| CU | 8 | 1.2% |
| ECU | 6 | 0.9% |

### Camera Angle Distribution

| Class | Frames | % |
|-------|--------|---|
| NEU | 369 | 56.8% |
| HIGH | 252 | 38.8% |
| LOW | 29 | 4.5% |

### Source Data

- **Domain**: Professional Soccer Broadcasts (Serie A via Infront Italy)
- **Resolution**: 1280×720 (720p)
- **Annotation Style**: Per-frame JSON
- **Labeling Method**: Proprietary model + human verification (100% verified)


## Dataset Structure

```
infactory-ai/camera-detection/
├── README.md
├── metadata.csv
├── dataset_info.json
├── taxonomy/
│   ├── shot_scale.json
│   ├── camera_angle.json
│   └── mappings.json
├── schema/
│   └── annotation_schema.json
└── data/
    └── frames/
        └── {uuid}_{frame_number}.jpg
```

### Metadata Fields (`metadata.csv`)

| Field | Type | Description |
|-------|------|-------------|
| `frame_id` | string | Unique frame identifier (UUID) |
| `source_video_id` | string | UUID of the source video clip |
| `frame_number` | int | Frame index in the source clip |
| `timestamp_sec` | float | Timestamp in seconds |
| `shot_scale` | string | Shot scale label (ECU, CU, MCU, MS, MLS, LS, ELS, FS, IS) |
| `shot_scale_code` | int | Numeric code (0–8) |
| `camera_angle` | string | Camera angle label (OVH, HIGH, NEU, LOW, DUTCH) |
| `camera_angle_additional` | string | Additional angle labels if multi-label (pipe-separated) |
| `subject_type` | string | Primary subject type |
| `camera_position` | string | Physical camera position |
| `confidence` | float | Model confidence before human verification |
| `human_verified` | bool | All frames are human-verified |

## Usage

### Loading with Hugging Face Datasets

```python
from datasets import load_dataset

dataset = load_dataset("infactory-ai/camera-detection")
```

### Loading with pandas

```python
import pandas as pd

df = pd.read_csv("metadata.csv")
print(df["shot_scale"].value_counts())
print(df["camera_angle"].value_counts())
```

## Team

| Name | Role |
|------|------|
| **Valentino Constantinou** | Head of Infrastructure |
| **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{camera_detection_2026,
  title={Soccer Camera Detection Dataset},
  author={Constantinou, Valentino and Kanalakis, John},
  year={2026},
  publisher={Infactory},
  url={https://huggingface.co/datasets/infactory-ai/camera-detection},
  note={Per-frame camera characterization: 9-class shot scale (CineScale-aligned), 5-class camera angle (CineScale2-aligned)}
}
```