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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchStores can detect a possible missed scan by comparing what a shopper handles with the checkout’s scan records. Depending on the system, cameras and computer vision track items near the scanner and bagging area, while checkout software, scale readings or other transaction data help identify a mismatch. An alert signals something to check; it does not establish that a shopper intended to steal.
What a missed-scan system looks for
The central clue is a mismatch between an item’s movement and the transaction. A product may pass through the checkout area or into a bag without a corresponding scan, or the item handled may not appear to match the product recorded by the scanner.
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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 linked with self-checkout software, such a system can check whether the expected scan occurred or was incomplete. The report identifies patterns including scanning one item from a multipack, leaving an item in a hand or basket, and starting a transaction with an item already on the output area.
What information can be combined
There is no single sensor configuration used by every store. A system may combine video with point-of-sale or self-checkout events, and some approaches also use security scales or other transaction data. A camera or scale by itself is a component, not a complete missed-scan detection system.
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- Video and computer vision: Cameras capture item handling; software analyzes whether an item appears to move past the scanner or into the bagging area.
- Checkout integration: Transaction events let the system compare what appears to happen on video with what the kiosk records as scanned.
- Scales and other data: Depending on the installation, scale readings or other transaction information may provide additional signals.
Vendor pages describe different implementations. Diebold Nixdorf describes computer vision, security scales, USB or IP cameras and integration with checkout software, with possible shopper prompts, staff alerts or checkout blocks. Hikvision’s SCO-Check describes real-time analysis of network-camera video for scan avoidance and non-payment, with exception alerts to staff. These are descriptions of vendors’ own products, not independent comparisons of their accuracy.
What happens after a system flags an event
The response depends on the retailer’s setup. A system may prompt the shopper to rescan or check an item, notify an employee, pause or block the transaction, or send a video clip for review. For example, Panoptyc describes using existing cameras and store context to identify possible loss events, with a person reviewing an event before an alert reaches store staff. Its claims about its service are vendor claims; camera coverage must include the area relevant to the event.
A flag is not proof of intent. Diebold Nixdorf’s report distinguishes intentional non-scanning from unintentional error, and its examples include ordinary checkout mistakes such as leaving an item in a basket. A discrepancy can justify a closer look without showing why it happened.
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How lane monitoring differs from exit receipt checks
Some stores use a separate process after payment: they compare what appears to be in a cart with the receipt. That is not the same as detecting a missed scan while a shopper is using the kiosk. Lane monitoring looks at item handling alongside scan events; exit verification checks whether cart contents appear to match a completed purchase.
Sam’s Club and Walmart Global Tech describe an exit workflow in which cameras image a passing cart, computer vision identifies products and their features, and the system matches the cart to an outstanding receipt. It then compares the apparent contents with the receipt 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.
In an April 30, 2024 announcement, Sam’s Club said the system had reached more than 120 clubs. The company reported that, at clubs where it was deployed, more than half of members received the friction-free exit experience and all members were leaving 23% faster. Those are the retailer’s reported results for that deployment and date, not a measure of self-checkout lane detection.
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What retailer adoption figures do—and do not—show
Zebra Technologies’ 2024 study announcement reported that 45% of retailers planned to use self-checkout cameras and sensors for loss prevention within the next three years, 46% planned to use computer vision, and 42% planned to use RFID tags and readers. The study was a global online survey of more than 4,200 shoppers, retail decision-makers and associates. These percentages describe planned use, not verified adoption rates or proof of system performance.
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The cited vendor descriptions explain how particular systems are intended to work, but they do not establish independent detection accuracy, false-positive rates or a reliable ranking across products. Performance can depend on camera position, store layout, product mix and how well the video system connects to checkout records. The available descriptions therefore support explaining the detection approach, not claiming that every alert is correct or that one named product is best.
Amazon’s Just Walk Out system offers related context but is not a template for ordinary self-checkout: it is a checkout-free system that uses cameras, weight sensors and AI to infer what shoppers select. Amazon’s explainer quotes AWS senior manager of Applied Science Chris Broaddus: “Figuring out the ‘what’ and how many items were taken by a shopper is a challenging AI problem to solve.” That observation concerns Just Walk Out, not every retailer’s self-checkout equipment.
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