Monitoring Browsing Behavior of Customers in Retail Stores via RFID Imaging
Kamran Ali, Alex X. Liu, Eugene Chai, Karthik Sundaresan

TL;DR
This paper introduces TagSee, a novel RFID imaging system that uses monostatic RFID signals and deep learning to monitor and identify customer browsing behavior in retail stores, achieving high accuracy with minimal training data.
Contribution
The paper presents a new RFID imaging approach combining analytical modeling and deep learning to monitor multiple customers' browsing behavior using commercial RFID devices.
Findings
Achieves over 90% true positive rate in multi-person scenarios.
Maintains less than 10% false positive rate.
Operates effectively with training data from only 3-4 users.
Abstract
In this paper, we propose to use commercial off-the-shelf (COTS) monostatic RFID devices (i.e. which use a single antenna at a time for both transmitting and receiving RFID signals to and from the tags) to monitor browsing activity of customers in front of display items in places such as retail stores. To this end, we propose TagSee, a multi-person imaging system based on monostatic RFID imaging. TagSee is based on the insight that when customers are browsing the items on a shelf, they stand between the tags deployed along the boundaries of the shelf and the reader, which changes the multi-paths that the RFID signals travel along, and both the RSS and phase values of the RFID signals that the reader receives change. Based on these variations observed by the reader, TagSee constructs a coarse grained image of the customers. Afterwards, TagSee identifies the items that are being browsed…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsEmirates Airlines Office in Dubai
