AI video analytics use case
AI abandoned object detection
Short answer
Abandoned object detection is video analytics that raises an alert when an item, such as a bag, box or package, appears in a monitored area and stays there unattended beyond a set time. It helps security teams respond quickly to unattended items in concourses, lobbies, platforms and entrances, where they may indicate a risk.
How abandoned object detection works
The system models the normal background of the scene and detects new objects that appear and then remain static. Object-detection models classify the item, and tracking checks whether the person who left it has moved away. If the item stays unattended beyond the configured time, an alert is raised with an image or short clip of the item.
Zones and time limits are set per area, so items that are routinely put down for a while, such as luggage at a check-in counter, do not raise alerts.
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.
- Busy scenes: heavy foot traffic can hide items behind passing people and interrupt tracking.
- Camera placement: elevated views that see the floor clearly work best.
- Lighting: changing light and shadows can make the background model see objects that are not there.
- Time limits: limits should reflect how long items are normally left, for example at a baggage counter.
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
Abandoned Object 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
Abandoned Object Detection: common questions
How long must an item be left before an alert?
That is configured per zone by the site team, based on how long items are normally set down in that area. A short limit suits a platform edge or a building entrance; a longer one suits a waiting area or baggage counter, where people often put bags down for a while.
Does abandoned object detection work in crowded areas?
It can, but heavy foot traffic hides items behind passing people and interrupts tracking, which makes busy scenes harder. Elevated camera views that see the floor clearly, well-chosen zones and realistic time limits improve results. Each camera view is assessed during solution design before the rule goes live.
Can existing cameras be used?
Often, yes. OrionAlerts works with existing IP cameras through ONVIF, RTSP and vendor SDKs, with compatibility confirmed per camera model during solution design. The best cameras for this use case are mounted high and look down on the floor, so items are not hidden behind people.
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.