Detection and tracking of shopping groups in stores

Abstract

We describe a monocular real-time computer vision system that identifies shopping groups by detecting and tracking multiple people as they wait in a checkout line or service counter. Our system segments each frame into foreground regions which contains multiple people. Foreground regions are further segmented into individuals using a temporal segmentation of foreground and motion cues. Once a person is detected, an appearance model based on color and edge density in conjunction with a mean-shift tracker is used to recover the person's trajectory. People are grouped together as a shopping group by analyzing interbody distances. The system also monitors the cashier's activities to determine when shopping transactions start and end. Experimental results demonstrate the robustness and real-time performance of the algorithm.