19 March 2013 Video-CRM: understanding customer behaviors in stores
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This paper describes two real-time computer vision systems created 10 years ago that detect and track people in stores to obtain insights of customer 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 customer 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 customer behavior and extract actionable real-time retail analytics.
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Ismail Haritaoglu, Ismail Haritaoglu, Myron Flickner, Myron Flickner, David Beymer, David Beymer, "Video-CRM: understanding customer behaviors in stores", Proc. SPIE 8663, Video Surveillance and Transportation Imaging Applications, 86630Y (19 March 2013); doi: 10.1117/12.2007327; https://doi.org/10.1117/12.2007327

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