A retail security team cannot watch thousands of hours of camera footage in real time, so video analysis models score clips for likely incidents — theft, altercations, safety hazards — and flag the ones worth a guard's attention. Flag too much, and the review queue becomes unmanageable; flag too little, and a real incident goes unreviewed.
The AI Video Analysis Lab models 5,000 hours of retail security footage. Raising incident-flag sensitivity catches more true incidents, at the cost of flagging many benign clips a human still has to sit through and rule out.
The rarity of true incidents is what makes this threshold so sensitive to small changes — with incidents making up only a tiny fraction of all footage, even a modest sensitivity increase can multiply the review queue many times over for a comparatively small gain in incidents caught.
🧪 Try it yourself: the AI Video Analysis Lab simulation lets you move the incident-flag sensitivity and watch the weekly outcome update live.
🧪 Try it yourself: the AI Video Analysis Lab simulation lets you experiment with everything described above directly in your browser.