Detect missed scans and theft at self-checkouts
Retailers detect theft and missed scans at self-checkouts
Fits: Sales · Legal & compliance
The camera above the checkout notices when an item has been put down but not scanned, and asks on screen for it to be scanned again. Fewer items go through unpaid, and the checkout supervisor helps where needed instead of watching every purchase.
Is this for you?
- You have several self-checkouts and noticeable stocktaking differences for goods sold there.
- You want to correct mistakes by customers, who are mostly honest, in a friendly way, without facial recognition and without suspect lists.
- Your checkout provider offers camera-based recognition, or you can test it with them in one store.
Today, put six to eight items on the bagging area of a checkout, photograph them from above with no people in the picture and write down the list of scanned items, leaving one out on purpose. Use the template below to have the AI compare the two and see whether it finds the missing item. This shows on a small scale how the recognition works and where it fails.
How to go about it
- Define the checkout situations to be detected
- Set up cameras above the scanner and bagging area
- Record and label test purchases
- Test the recognition and set the intervention threshold
- Build in the on-screen prompt and help from staff
- Review false alarms and missed cases monthly
Templates to copy
The complete guide
- 6 steps explaining exactly what to do
- 3 proven templates to copy into your own AI
- Safe use: data, checking, deciding, informing
- Pitfalls, effort and how to recognise success
With the training you get all 430 guides for as long as your access runs.
Same approach, other use cases
24 more use cases follow the pattern “Inspecting by camera”.
EH-08 · Legal status 08.10.2026