README.md

---
license: cc-by-4.0
language:
- en
pretty_name: 'Fight Detection Dataset (CCTV, YOLO pose): free sample'
task_categories:
- keypoint-detection
- object-detection
tags:
- synthetic
- cctv
- security-camera
- surveillance
- computer-vision
- object-detection
- yolo
- fight-detection
- violence-detection
- aggression-detection
- pose-estimation
- keypoints
- action-recognition
- coco
- security
size_categories:
- n<1K
---

# Fight Detection Dataset (CCTV, YOLO pose): free 103-image sample

This **fight detection dataset** is a free sample of 103 synthetic CCTV images of people fighting or about to fight, with YOLO pose labels (bounding box plus 17 body keypoints) for every `person`, for training and testing fight, violence and aggressive-pose detection models on security-camera footage. The full 1,000-image dataset adds a `fight` box around each group in contact, about 28% look-alike negatives (hugs, handshakes, dancing, arguments) and day, dusk, night and IR views, in YOLO-pose and COCO with a 900 / 100 train-val split. Made by [Simuletic](https://simuletic.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=CCTV_Aggressive_Poses_Fight_Detection_Dataset), a Swedish company that has built synthetic CCTV datasets for computer vision since 2023.

**[Get the full dataset](https://simuletic.com/datasets/cctv-aggressive-pose-detection-dataset?utm_source=huggingface&utm_medium=referral&utm_campaign=CCTV_Aggressive_Poses_Fight_Detection_Dataset)** · Free sample licence: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). This free sample is also on [Kaggle](https://www.kaggle.com/datasets/simuletic/cctv-aggressive-poses-and-fight-detection-dataset) and [GitHub](https://github.com/Simuletic/cctv-aggressive-poses-fight-detection-dataset).

## Facts

| | Free sample (this repo) | [Full dataset](https://simuletic.com/datasets/cctv-aggressive-pose-detection-dataset?utm_source=huggingface&utm_medium=referral&utm_campaign=CCTV_Aggressive_Poses_Fight_Detection_Dataset) |
|---|---|---|
| Images | 103 JPEG | 1,000 (900 train / 100 val) |
| Classes | `person` (one class) | `person` with 17 keypoints, plus `fight` boxes |
| Boxes | 207 person, each with 17 keypoints | every person boxed; one fight box per group in contact |
| Hard negatives | none | about 28%: hugs, handshakes, high fives, dancing, arguments without contact |
| Resolution | 784 × 1,168 to 1,206 × 1,560 px | CCTV frames; day, dusk, night and monochrome IR |
| Camera view | elevated CCTV angles, indoor and outdoor | streets, car parks, nightlife areas, stations, shops, lobbies, concourses |
| Labels | YOLO pose: box + 17 COCO keypoints | YOLO-pose + COCO, dataset card |
| Licence | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | one-time: research $149 / commercial $590 per 1,000 images |

## What's in the images

People squaring up, shoving, punching or about to fight, seen from elevated CCTV cameras outside pubs, in car parks, shopping arcades, underpasses and streets, by day and at night. 102 images show two people and one shows three: 207 people in total. Every person has a bounding box and a 17-point body skeleton, so models can learn body language and pre-fight cues, not just where people are. In this sample every person has the same class, `person`.

## Classes and boxes

| Class | YOLO id | Boxes in sample |
|---|---|---|
| `person` | 0 | 207 |

## Label format and folder layout

```text
Aggressive_Poses_Dataset/
  data.yaml                # class name and keypoint shape
  images/                  # 103 JPEG images (aggressive_pose_1.jpg, ...)
  labels/                  # one YOLO pose .txt per image
```

Each label line is one person: `class_id x_center y_center width height`, then 17 keypoints as `x y v` (56 values in total). Coordinates are normalised to 0–1. The keypoints follow the COCO order (nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles); `v` is 2 for a labelled keypoint and 0 for one that is not labelled. Shortened example:

```text
0.000000 0.310 0.675 0.379 0.644  0.391 0.427 2.000000  ...  0.180 0.938 2.000000
```

Values are written as decimals (`0.000000` for class 0, `2.000000` for visible); Ultralytics reads them as normal.

## Quick start (Ultralytics YOLO)

Create a `data.yaml` next to the `Aggressive_Poses_Dataset/` folder:

```yaml
path: Aggressive_Poses_Dataset
train: images
val: images   # small sample: use the same images, or split them yourself
kpt_shape: [17, 3]
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
names:
  0: person
```

Then train:

```python
from ultralytics import YOLO

model = YOLO("yolo11n-pose.pt")
model.train(data="data.yaml", epochs=30, imgsz=640)
```

## Use cases

- Fight and violence detection on CCTV and video management systems (VMS)
- Early warning of confrontations in nightlife venues, public transport and schools
- Pose-based action recognition and body-language research
- Pre-training before fine-tuning on your own camera footage

## Full dataset and prices

The full **[CCTV Fight & Aggression Detection Dataset](https://simuletic.com/datasets/cctv-aggressive-pose-detection-dataset?utm_source=huggingface&utm_medium=referral&utm_campaign=CCTV_Aggressive_Poses_Fight_Detection_Dataset)** has 1,000 images (900 train / 100 val), YOLO-pose + COCO, person boxes with 17 keypoints plus fight boxes, about 28% look-alike negatives. Every licence is a one-time payment, perpetual, for one organisation; you get a download link within five working days (card or invoice). Research covers students and non-commercial work and is available for 1,000 images only; packs of 5,000 images and more are sold with the commercial licence, which lets you train models and ship them in products.

| | Research | Commercial |
|---|---|---|
| 1,000 images | $149 | $590 |
| 5,000-image pack | not offered | $2,400 |
| 10,000-image pack | not offered | $4,500 |
| 20,000-image pack | not offered | $8,500 |

Packs are the same dataset at larger size, with the same labels and split, under the commercial licence. 50,000+ images, video datasets and enterprise agreements (from $10,000) are quoted. Other scenes, camera angles or classes: [custom data](https://simuletic.com/custom-data?utm_source=huggingface&utm_medium=referral&utm_campaign=CCTV_Aggressive_Poses_Fight_Detection_Dataset) or contact@simuletic.com.

## How the images were made

The images are fully synthetic (computer-generated) by [Simuletic](https://simuletic.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=CCTV_Aggressive_Poses_Fight_Detection_Dataset): no real people, places or incidents are depicted, so there are no privacy or consent issues. 

**Limitations.** Synthetic images do not cover every camera, lens, compression artefact or scene you will meet in deployment. Validate on your own footage, and for production combine synthetic and real data. This sample labels everyone as `person`; the full dataset separates `normal_pose` from `aggressive_pose`.

## More synthetic datasets from Simuletic

- [CCTV Fall & Incident Detection Dataset](https://huggingface.co/datasets/Simuletic/CCTV_Incident_Dataset_Fall_Lying_Down_Detection)
- [CCTV Knife Detection Dataset](https://huggingface.co/datasets/Simuletic/cctv-knife-detection-dataset)
- [CCTV Weapon Detection Dataset](https://huggingface.co/datasets/Simuletic/cctv-weapon-dataset)
- [CCTV Pedestrian-1K: Person Attribute Dataset](https://huggingface.co/datasets/Simuletic/CCTV-Pedestrian-1K-Person-Attribute-Dataset)
- All datasets: [simuletic.com/datasets](https://simuletic.com/datasets?utm_source=huggingface&utm_medium=referral&utm_campaign=CCTV_Aggressive_Poses_Fight_Detection_Dataset) · [Hugging Face](https://huggingface.co/Simuletic) · [Kaggle](https://www.kaggle.com/simuletic) · [GitHub](https://github.com/Simuletic)

## Licence

This free sample is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/): you may share and adapt it for any purpose, including commercial use, as long as you credit Simuletic. The full dataset is sold under a separate one-time research or commercial licence; it is not redistributed.

## Citation

```bibtex
@misc{simuletic_fight_2025,
  author       = {{Simuletic}},
  title        = {CCTV Fight & Aggression Detection Dataset},
  year         = {2025},
  publisher    = {Simuletic},
  howpublished = {\url{https://huggingface.co/datasets/Simuletic/CCTV_Aggressive_Poses_Fight_Detection_Dataset}},
  note         = {Free sample. Full dataset: \url{https://simuletic.com/datasets/cctv-aggressive-pose-detection-dataset}}
}
```

Questions or feedback: contact@simuletic.com