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What a missed-scan system looks for
The basic signal is a mismatch: an item appears to move toward or into the bagging area without a corresponding scan, or the item handled does not match the item recorded by the scanner. Diebold Nixdorf’s 2024 Cybersecurity Threat Report describes computer-vision systems that track products in shelves, bags, baskets and carts as shoppers move them toward the bagging area. When connected to self-checkout software, those systems can check whether an expected scan occurred or was incomplete. Diebold Nixdorf’s report identifies examples such as scanning one item from a multipack, leaving an item in a hand or basket, or having an item already on the output area when a transaction begins.
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The exact setup varies. Diebold Nixdorf describes combinations of computer vision, POS or self-checkout integration, security scales, and USB or IP cameras. Hikvision’s SCO-Check solution describes analyzing network-camera video for scan avoidance or non-payment and sending exception alerts to staff. These pages explain vendor products; they are not independent tests comparing accuracy.
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What can happen after the system flags an event
A flag is a signal for a response, not a verdict. Depending on the retailer’s system and procedures, the response may be a prompt to the shopper, an alert to an employee, a checkout pause or block, or a video clip routed for review. Panoptyc says its workflow can analyze existing camera coverage, have a person review a possible event, and then alert store staff. That is a description of Panoptyc’s service, not proof that every store uses human review. Panoptyc’s product information says the relevant checkout or other area must be visible to the cameras.
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A possible non-scan can be intentional or accidental. Diebold Nixdorf’s report explicitly distinguishes theft from unintentional error, and its examples include ordinary checkout mishaps. Camera footage or an automated alert may help an employee investigate what happened, but the alert alone does not establish intent.
How lane monitoring differs from an exit receipt check
Some retailers use a separate process after payment: they compare what appears to be in a cart with the completed receipt. Sam’s Club and Walmart Global Tech describe an exit system in which cameras image a passing cart, computer vision identifies products and their features, the system matches the cart to an outstanding receipt, then compares the items and sends information to an exit associate. Walmart says the process takes seconds and that its models are trained on images of its own products in different positions and lighting. Sam’s Club’s announcement and Walmart Global Tech’s explanation describe this cart-to-receipt check.
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This is not the same as monitoring whether each item was scanned at the kiosk. It checks whether cart contents appear to match a purchase after checkout. Sam’s Club reported on April 30, 2024, that the system had reached more than 120 clubs. In clubs where it was deployed, the company said more than half of members received a friction-free exit experience and all members were leaving 23% faster. Those are Sam’s Club’s reported results for that deployment, not independent measurements of lane-level missed-scan detection.
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A 2024 Zebra Technologies global online study surveyed more than 4,200 shoppers, retail decision-makers and associates. Its newsroom summary said retailers planned, within the next three years, to use the following technologies for loss prevention:
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| Technology | Share reporting plans to use it | What the figure means |
|---|---|---|
| Self-checkout cameras and sensors | 45% | Reported future intention, not measured adoption or proof of performance |
| Computer vision | 46% | Reported future intention, not measured adoption or proof of performance |
| RFID tags and readers | 42% | Reported future intention, not measured adoption or proof of performance |
The figures are from Zebra’s 2024 study announcement. They describe what respondents planned to do, not how many stores had already installed the systems or how accurately those systems detect a missed scan.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the public evidence can—and cannot—tell you
Published vendor descriptions explain possible inputs and workflows, but the sources cited here do not establish independent accuracy benchmarks, false-positive rates or a comparative ranking of the named products. Results may depend on camera placement, store layout, product mix and connection to the transaction system. Cameras and security scales are components; they do not, on their own, constitute a complete missed-scan detection system.
Amazon’s Just Walk Out system offers related context, not a model for every self-checkout lane. It is a checkout-free system that uses cameras, weight sensors and AI to determine what shoppers select. Amazon Web Services senior manager Chris Broaddus described the challenge this way: “Figuring out the ‘what’ and how many items were taken by a shopper is a challenging AI problem to solve,” in Amazon’s Just Walk Out lab explainer. That statement concerns Amazon’s system and should not be read as a description of the equipment or workflow in a typical self-checkout area.
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