AI video analytics use case
AI fall detection from CCTV cameras
Short answer
AI fall detection is video analytics that recognizes when a person falls, or is lying on the ground after a fall, in camera footage and raises an alert. It helps responders reach people sooner in places such as staircases, corridors, healthcare facilities, industrial floors and lone-working areas where a fall might otherwise go unnoticed.
How fall detection works
The model first detects people in the frame, then analyzes body posture and how it changes over time, for example a rapid transition from upright to horizontal, or a person remaining on the ground. Pose-estimation models that track key body points are commonly used for this.
Rules can limit detection to specific zones and schedules, and can require a person to remain down for a short period before alerting, to separate falls from people deliberately sitting or kneeling.
In OrionAlerts
In OrionAlerts, the AI runtime reads decoded frames from the same video engine that records the cameras, so detection runs on the streams you already record rather than on a parallel feed into a separate analytics appliance. Inference runs on the on-site Nebula OS node with GPU acceleration, and each event is written to the same record as the camera and site it came from. Alerts reach the dashboard, mobile app and email.
What affects reliability
Detection quality depends on the scene as much as the model. These are the factors to check during site survey and pilot.
- Camera angle: elevated views that see the whole body work better than views where furniture or equipment hides the floor.
- Occlusion: crowded scenes, shelving and machinery can hide part of the person or the fall itself.
- Lighting: poorly lit corridors and stairwells reduce how clearly posture can be recognized.
- Resolution and distance: people far from the camera occupy few pixels, which makes posture harder to read.
What you need
No camera replacement is assumed. A typical OrionAlerts deployment needs:
- Existing IP cameras that expose video over ONVIF, RTSP or a supported vendor SDK. Compatibility is confirmed per camera model during solution design.
- An on-site Nebula OS node with GPU acceleration, sized per deployment to the channel count and the detections you run.
- Camera views that actually cover the area to be monitored, with zones and schedules defined for each rule.
- Optional: Orion Cloud for a central view of alerts and health across multiple sites, without uploading raw video.
Where it is used
This use case commonly applies in the industries below. Each industry page covers the risks, detections and modules that matter for that kind of site.
Solution
Which solution it belongs to
Fall Detection is part of AI Vision & Real-Time Alerts: turn live video into real-time AI alerts for safety, intrusion, PPE, and operational events.
Explore AI Vision & Real-Time AlertsFAQ
Fall Detection: common questions
How does video fall detection tell a fall from someone sitting down?
It looks at how body posture changes over time. A fall usually shows a sudden move from upright to horizontal, while sitting or kneeling is slower and more controlled. Rules can also require the person to stay on the ground for a short period before alerting, which further separates falls from deliberate movements.
Where is fall detection most useful?
Fall detection is most useful where a fall might not be seen straight away: staircases, corridors, healthcare and senior-care areas, industrial floors, warehouses and lone-working areas. It works best where cameras see the whole body and the floor clearly, without furniture, shelving or machinery blocking the view.
Who receives OrionAlerts fall alerts?
The teams you configure for each site and zone, such as security, nursing or EHS staff. OrionAlerts delivers each alert to the dashboard, mobile app and email with its video clip and the camera and site it came from, so the responder knows where to go. Zone and schedule rules decide which events raise an alert.
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Plan this use case for your sites
We will review your camera views for this use case, scope the deployment for your sites and show it in a live demo.