Beyond Detection: How Edge AI People Counters Actually Work End to End

Building a functional edge AI people counter requires five distinct stages beyond simple person detection: frame capture, image preprocessing, detection, multi-frame tracking, and conversion of movement into entry or exit events. Each stage introduces potential errors, from uneven frame delivery and resizing artifacts to trackers assigning new IDs mid-doorway, all of which compound into inaccurate final counts. A tracker assigns temporary IDs to keep detections linked to the same individual across frames, enabling a line-crossing algorithm to register a visit only when a tracked path genuinely transitions from one side to the other. Occupancy figures are derived as running state — prior occupancy plus entries minus exits — meaning a single missed event corrupts the count until manually corrected. The article emphasizes that most demos stop at detection and frame-rate display, obscuring the substantial engineering needed before a system can reliably tell a retailer how many visitors entered a store.