dd0914/CCTV_Incident_Dataset_Fall_Lying_Down_Detection
Overview This is an open-source synthetic dataset for Computer Vision (CV) tasks, specifically designed for Fall Detection, Pose Estimation, and Incident Monitoring from overhead CCTV perspectives. Unlike standard object detection datasets, this dataset includes Keypoints (Pose) annotations. This enables models to understand human posture and accurately distinguish between standing and fallen individuals. 🚀 Need more data? This is a sample dataset by Simuletic. We provide… See the full description on the dataset page: https://huggingface.co/datasets/dd0914/CCTV_Incident_Dataset_Fall_Lying_Down_Detection.
Overview
This is an open-source synthetic dataset for Computer Vision (CV) tasks, specifically designed for Fall Detection, Pose Estimation, and Incident Monitoring from overhead CCTV perspectives.
Unlike standard object detection datasets, this dataset includes Keypoints (Pose) annotations. This enables models to understand human posture and accurately distinguish between standing and fallen individuals.
🚀 Need more data? This is a sample dataset by Simuletic. We provide hyper-realistic synthetic data to solve edge cases in AI training. For larger, more diverse datasets or video sequences, visit simuletic.com/datasets.
Key Features
- Dual Annotations: Includes both Bounding Boxes and 17-Keypoint Skeletons (COCO standard layout).
- Targeted Scenarios: Focused heavily on incidents (approx. 95% of subjects are "laying"/fallen), making it ideal for training anomaly detection models.
- Privacy-First: Fully synthetic. No real individuals are depicted, eliminating GDPR/privacy risks.
- Compatibility: Ready for YOLOv8-Pose and YOLO11-Pose models.
Dataset Structure & Classes
The dataset follows the YOLO Pose format.
- images/: High-quality synthetic .jpg/.png files.
- labels/: .txt files containing class ID, bounding box, and keypoints.
Class Map
0: laying (Person lying down / fallen)1: standing (Person standing / walking)
YAML Configuration
To use this with YOLO, your data.yaml should look like this:
path: /path/to/dataset train: images val: images
Keypoints shape (17 points, x/y/visibility)
kpt_shape: [17, 3]
names: 0: laying 1: standing
Use Cases
Fall Detection: Train models to detect slip-and-fall accidents in real-time using pose analysis.
Healthcare Monitoring: Detect patients falling in hospitals or care homes (privacy-safe).
Public Safety: Identify medical emergencies or potential crime victims in surveillance feeds.
Ethics & License
Synthetic Nature: This data is computer-generated. No real humans were recorded or harmed.
License: CC BY 4.0. You are free to use and adapt this data, provided you give appropriate credit to Simuletic.
Citation
If you use this dataset in your research, please cite:
@dataset{simuleticfalldetection_2025, author = {Simuletic Team}, title = {Simuletic Synthetic Fall & Incident Detection Dataset}, year = {2025}, url = {https://simuletic.com} }