AI video analytics use case
AI vehicle detection for gates, yards and restricted approaches
Short answer
Vehicle detection is video analytics that identifies cars, trucks, two-wheelers and other vehicles in camera footage and tracks their presence and movement. It alerts on vehicles entering restricted approaches, stopping in no-parking or emergency zones, or moving in the wrong direction, and supports monitoring of gates, yards, docks and internal roads.
How vehicle detection works
An object-detection model locates vehicles in each frame and classifies them by type, and a tracker follows them across frames. Rules then check vehicle behavior against zones and lines drawn on the camera view: entering a zone, crossing a line in a given direction, or remaining stationary beyond a time limit.
Vehicle detection is distinct from ANPR, which reads the number plate. The two are often combined where vehicles need to be identified as well as detected.
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 placement: views should cover the full approach, gate or zone, not only part of a lane.
- Lighting: headlight glare at night and deep shadows can hide or distort vehicles.
- Occlusion: queued or parked vehicles can hide others behind them.
- Distance and resolution: small or distant vehicles are harder to classify by type.
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
Vehicle 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
Vehicle Detection: common questions
Is vehicle detection the same as ANPR?
No. Vehicle detection finds vehicles, classifies them by type and tracks how they move. ANPR (automatic number plate recognition) reads the number plate as text to identify a specific vehicle. The two are often combined at gates and access points, where a site needs to both detect and identify vehicles.
What vehicle events can trigger alerts?
Typical events are a vehicle entering a restricted approach or zone, crossing a line in the wrong direction, or stopping where stopping is not allowed, such as a fire lane or a loading-bay entrance. Each rule is set per zone and schedule to match how traffic actually flows on the site.
Can vehicle detection run on existing yard cameras?
Often, yes. OrionAlerts connects existing IP cameras through ONVIF, RTSP and vendor SDKs, provided the views cover the gates, roads or yard areas to monitor. Compatibility is confirmed per camera model during solution design, and night views with strong headlight glare are checked during the site survey.
Related use cases
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.