AI object detection
Also known as: AI-based object detection · video analytics
AI object detection is software that automatically recognizes and distinguishes people, vehicles, or objects in a video feed, not just movement.
Classic motion detection simply compares pixels between two frames and flags any change, whether it's a person, a cat, or a moving shadow. AI object detection uses trained models to identify and outline specific object classes in the image, such as a person, a vehicle, or a piece of luggage. That makes it possible to limit an alert to certain object types or specific areas of the frame.
In practice, AI object detection runs either directly on the camera, on the NVR, or on a separate server, depending on processing power and channel count. It shows up as bounding boxes around detected objects in the live view, and as the option to configure alerts for something like 'person enters zone' instead of any movement at all. Some systems also support line-crossing detection or counting objects within an area.
A common misunderstanding: AI object detection doesn't reliably recognize every object under all conditions. Poor lighting, an awkward camera angle, or objects that weren't part of the training data still lead to false alarms or missed events. It doesn't replace thoughtful camera placement and configuration, it builds on top of it.
What it means in practice
In practice, AI object detection mainly shows its value by cutting false alarms triggered by wind, rain, or passing traffic, which is often the only reason footage actually gets reviewed at all. NDVDL deploys it where plain motion detection produces too many irrelevant events given the surroundings.
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