Theft Detection

What it does

A retail loss-prevention system built on custom-trained YOLO detectors. Rather than relying on generic object classes, we fine-tuned detectors on the client's own shop-floor footage — open/closed cash drawers, hands, and garments — so the system can recognise the specific sequences that precede theft: reaching into a drawer, concealing an item, grab-and-go. Flagged events are saved as clips for staff review.

How it works

1. Step 1. Custom YOLO models fine-tuned on the store's own footage detect drawers, hands, people and merchandise in each CCTV frame.
2. Step 2. The system tracks interactions between them over time — a hand entering an open drawer, an item leaving the shelf without reaching the counter.
3. Step 3. Suspicious sequences (concealment, drawer tampering, grab-and-go) raise a theft score.
4. Step 4. Crossing the threshold saves a timestamped clip and raises an alert for review.

Where it's used

• Retail loss prevention
• Cash-drawer monitoring
• Self-checkout monitoring
• Warehouse security

Tech stack

• Python
• Ultralytics YOLO (fine-tuned)
• OpenCV
• Custom datasets

Build something like this

This project is part of our computer vision development and restricted-zone monitoring. Tell us your use case and we will reply within one business day with a first step, a price and a date.