Fast detection of retail fraud using polar touch buttons
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.