People (High)

Input and output format for the default People Detection (High Accuracy) demo model.

This AI model, based on YOLOv11-small (YOLOv11s), is trained to detect people in a given image. It runs at a higher input resolution than the Low Accuracy variant, improving detection of small or distant people at the cost of higher compute requirements. Use this model when detection accuracy matters more than frame rate or hardware resources.

Input

The input tensor dimension for this model is 640 wide by 640 high.

This model accepts the following extra input information:

• Mask: excludes or includes a specific region of the camera view.

• Sensitivity threshold: NMS sensitivity.

Output

Each detected person is identified using a bounding box represented by a vector: [x1, y1, x2, y2, score, class]. The object bounding boxes contain the following information:

• Position and size coordinates, to be used as a rectangular bounding box.

• A confidence score, which helps retain only the most confident and accurate detections while suppressing weaker or redundant ones.

• Class, in this case always "Person".

The following image shows an example of this model's output:

Example people detection output in the desktop client

Postprocessors

This model's detections can be combined with any of the built-in postprocessors, for example:

• Loitering Detection: detect when an object stays in frame longer than a configured time threshold.

• Left Behind Object Detection: detect objects left behind or removed compared to a reference frame.

• Line Crossing Detection: detect or count objects that cross a defined line, and generate an event.

• Object Counting: count bounding boxes per class and generate a counting event.

See Postprocessors for the full list and configuration details.