Computer Vision
How AI People Counting Works
How camera position, detection, tracking, counting lines, calibration and reporting shape a people-counting system.
People counting estimates movement through a defined view. Accuracy depends as much on entrance geometry, camera position and evaluation method as on the detection model.
Detect, track and cross a line
A model detects people in each frame, a tracker associates detections over time, and the system records a directional event when a track crosses a calibrated line or zone. Aggregated events become hourly or daily counts.
Environment defines the ceiling
Severe occlusion, groups moving side-by-side, shadows and poor camera angles reduce reliability. A site assessment should define the expected operating range and the situations that need manual review.
Design the output around decisions
Most teams need trends, entrance comparisons and occupancy indicators rather than identity. Retention, access and image handling should be minimised to what the use case genuinely requires.
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