Simuletic/CCTV_Incident_Dataset_Fall_Lying_Down_Detection

CCTV Fall & Incident Detection Dataset (free sample)

A free sample of 111 synthetic CCTV images of people lying on the ground after a fall, with YOLO pose labels (bounding box plus 17 body keypoints) for laying and standing, for training and testing fall detection and pose estimation models on security-camera footage. Made by Simuletic, which builds synthetic CCTV datasets for computer vision.

Full dataset and licence: simuletic.com/datasets/cctv-fall-incident-detection-dataset

	Free sample (this page)	Full dataset
Images	111 PNG, 1,024 × 1,024 px	1,000 (900 train / 100 val)
Classes	laying (fallen), standing	person, fallen_person
Labels	YOLO pose: box + 17 COCO keypoints	YOLO + COCO boxes
Camera view	elevated CCTV angles, many wide-angle; indoor and outdoor	ceiling and wall mounts, day and night; about 20% hard negatives
Licence	CC BY 4.0	one-time licence: research $149 / commercial $590

What's in the images

People lying on the ground after a fall, seen from elevated CCTV cameras: in shop aisles, on streets and pavements, and in other indoor and outdoor places. Many views are wide-angle. Every image has one person lying down (laying); 10 images also show a person standing or walking in the background (standing). Each person has a bounding box and a 17-point body skeleton, so you can train either a detector or a pose model that tells lying from standing.

Classes and boxes

Class	YOLO id	Boxes in sample
laying	0	111
standing	1	10

Folder layout

laying_dataset/
  dataset.yaml             # class names and keypoint shape
  example_image.png        # preview image (not labelled)
  images/                  # 111 PNG images (laying01.png, ...)
  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:

0 0.579 0.658 0.346 0.398  0.451 0.477 2  0.452 0.469 2  ...  0.491 0.813 2

Quick start (Ultralytics YOLO)

Create a data.yaml next to the laying_dataset/ folder:

path: laying_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: laying
  1: standing

The included dataset.yaml has path: /; use the folder path as above instead.

Then train:

from ultralytics import YOLO

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

Use cases

• Fall detection in care homes, hospitals and assisted living
• Slip-and-fall detection in shops, stations and workplaces
• Spotting people lying on the ground (medical emergencies) on public-space cameras
• Pose-based posture classification: lying versus standing
• Pre-training before fine-tuning on your own camera footage

How the images were made

The images are fully synthetic (computer-generated) by Simuletic: 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 uses pose labels with the classes laying and standing and has no hard negatives; the full dataset uses person and fallen_person boxes and adds hard negatives such as people sitting, crouching or kneeling.

Full dataset and custom data

The full CCTV Fall & Incident Detection Dataset has 1,000 images, YOLO + COCO, classes person, fallen_person. Licence: research $149 / commercial $590. Larger sets, other scenes, camera angles or classes: see custom data or write to contact@simuletic.com.

This free sample is also on Kaggle and GitHub.

More synthetic datasets from Simuletic

• CCTV Aggressive Pose & Fight Detection Dataset
• CCTV Pedestrian-1K: Person Attribute Dataset
• CCTV Smoke & Fire Emergency Detection Dataset
• CCTV Knife Detection Dataset
• All datasets: simuletic.com/datasets · Hugging Face · Kaggle · GitHub

Licence

This free sample is licensed under CC 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.

Citation

@misc{simuletic_fall_2025,
  author       = {{Simuletic}},
  title        = {CCTV Fall & Incident Detection Dataset},
  year         = {2025},
  publisher    = {Simuletic},
  howpublished = {\url{https://huggingface.co/datasets/Simuletic/CCTV_Incident_Dataset_Fall_Lying_Down_Detection}},
  note         = {Free sample. Full dataset: \url{https://simuletic.com/datasets/cctv-fall-incident-detection-dataset}}
}

Questions or feedback: contact@simuletic.com