Anyone who has been grocery shopping recently can't have failed to notice the numerous self-checkout lanes that many grocery stores are now installing in their premises as an alternative to traditional cashier-staffed checkouts.
The reason for this is quite simple. By enabling consumers to scan the barcodes on their own items, and manually identify items such as fruits and vegetables which are then weighed, stores can man a six station check out with just a single person, cutting down on costs considerably.
But by doing so, many stores have left themselves open to unscrupulous individuals who may either attempt to hoodwink such systems into believing that they are purchasing a lovely bunch of coconuts instead of a pack of somewhat more expensive sirloin steaks, or simply bag the items at the checkout without bothering to scan them at all.
Apparently, things have got so bad on the pilfery front, that New England-based Big Y (Springfield, MA, USA) has abandoned any more self-checkout ideas it had planned, citing both customer service as well as shoplifting behind its decision.
Fortunately, however, one company now believes it can offer a solution to the knotty problem -- a solution that is, of course, based around an intelligent vision analysis system.
That company, StopLift Checkout Vision Systems (Cambridge, MA, USA) has developed a computer vision system that can interpret the behavior of the customer by analyzing and understanding body motions at the checkout.
By analyzing the digitized video, the so-called “ScanItAll system” scrutinizes how each item is handled to determine whether or not it was properly scanned. The patented system is capable of understanding fraudulent behavior, including when a bar code is covered up by hand.
Of course, there are alternative approaches to cutting down fraud. One such approach which has been tested out by grocery retail giant Kroger (Cincinnati, OH, USA) involves the deployment of a tunnel called the Advantage Checkout. This system incorporates a battery of imaging scanners that read bar codes and letters and numbers on goods to identify them as they travel through the tunnel.
Eliminating the need for customers to scan their own goods could prove equally as effective, if not more so, at cutting down on theft as analyzing customers' movements from video footage. Combining the two approaches could mean that thieves are faced with a much tougher time should they try to purloin the sirloin in the future.
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