Abstract
This paper describes two real-time computer vision systems created 10 years ago that detect and track people in stores to obtain insights of behavior while shopping. The first system uses a single color camera to identify shopping groups in the checkout line. Shopping groups are identified by analyzing the inter-body distances coupled with the cashier's activities to detect checkout transactions start and end times. The second system uses multiple overhead narrow-baseline stereo cameras to detect and track people, their body posture and parts to understand interactions with products such as customer picking a product from a shelf. In pilot studies both systems demonstrated real-time performance and sufficient accuracy to enable more detailed understanding of behavior and extract actionable real-time retail analytics.
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