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Demonstrating just how far the potential to detect criminal actions, and errors, around the retail point of sale area has come is the software-based checkout vision system pioneered by StopLift (www.stoplift.com). The state-of-the-art ScanItAll solution automatically analyses CCTV video to interpret the behavior of a cashier and customer and, crucially, the handling of individual items moving through the checkout area. By mathematically analysing the pixels of digitized video it can be determined if an item has been properly scanned.
When discussing video surveillance in the retail environment, not surprisingly, much attention is given to the criminal actions of shoplifters and the impact this has on stock shrinkage. Sadly this is only part of the story. Behind-the-scenes retailers are also having to contend with the actions of dishonest staff who account for a significant proportion of total losses. Here we find out more about the extent of the problem and discover how CCTV can be employed, successfully, to tackle this issue in a way that keeps honest staff on-board while identifying the offending behaviour in their less scrupulous colleagues.
The busy Christmas shopping period is well underway and stores are packed with shoppers buying gifts and stocking up on festive food and drink. However…
StopLift’s ScanItAll™ recognition technology detected more than 130,000 ‘sweethearting’ and other scan avoidance incidents at the checkoput including many at the self-checkout, in 2010…
Many stores use closed-circuit television to watch checkout lines. But they often don’t have the time or manpower to review the tapes, so the cameras are not a strong deterrent. StopLift has devised a system that combines cameras with advanced software to spot sweethearting automatically…
The interface is very user friendly. It is really as simple as what you see.
If someone clicked on it without knowing the system, it would be easy for them to maneuver their way around the system – to view the data and the footage.
Wouldn’t take someone long to funnel down and find an incident.
