Fast detection of retail fraud using polar touch buttons

Abstract

Video analytics have recently emerged as a promising technique of retail fraud detection for loss prevention. Efficient video analytic algorithms are highly desired for a practical fraud detection system. In this paper, we present a real-time algorithm for recognizing a cashier's actions at the Point of Sale (POS), which can be further used to analyze cashier behaviors for identifying fraudulent incidents. The algorithm uses a set of simple but effective features derived from a global representation of motion energy called Polar Motion Map (PMM). These features capture the motion patterns exhibited in a cashier's actions as a focused beam of motion energy, characterizing the actions as the extension and retraction movement of the cashier's arm with respect to a prespecified region. Our algorithm demonstrates comparable accuracy against one of the state-of-the-art event recognition techniques [1] while running significantly faster.

Multimodal ranking for non-compliance detection in retail surveillance

“…There has been many works on retail video surveillance for sweethearting detection [14,4,1,5,6,15,7,11,8]. However, none of the approaches above employed a featurebased representation of nonscans.…”

Design considerations for multi-stakeholder display analytics

“…Number of potential viewers in display area [43,48,53]; time potential viewers spend in area [48]; and entrance and exit counts [48] Physical characteristics of viewers (hair type, eyewear, clothing colour) through surveillance cameras [25,50] People tracking within video [43]; re-identifying people [27,43]; pedestrian models [4]; frequent visitors [22]; fraud detection [17]; item interaction [32] Direction, speed and location [56]; multicamera tracking [57]; shopping cart tracking [46]; WiFi fingerprints [1] Number of sales [48,53]; conversion rate [48]; PoS events (refund, cancellation) [53] …”