Fall detection: Datasets & Models

In security and surveillance settings such as care facilities, hospitals, and monitored residences, falls often go unnoticed until a staff member happens to check a screen or find the person, and response delays can worsen outcomes. Manually watching dozens of camera feeds around the clock is impractical and prone to fatigue and human error. Automating fall detection with computer vision lets a model watch every camera continuously and raise an alert the moment a fall is detected, so responders are notified in seconds rather than minutes. This turns passive recording infrastructure into an active safety monitor.

The workflow runs on existing IP and CCTV cameras, so there is no need for wearables or floor sensors, and detections can trigger alerts to existing notification systems. It scales across many camera feeds simultaneously and can be retrained on your own footage to handle specific room layouts, lighting, and camera angles. Using RF-DETR for detection provides strong accuracy on people and posture while supporting real-time inference.

1. Upload an image: Drop any photo into the widget above to try fall detection for free, right on this page.
2. Preview instant results: The model detects person standing, person fallen, person lying on floor, person sitting, wheelchair within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.