Publication Date: 9/21/2020
Event: The 26th Annual International Conference on Mobile Computing and Networking (MobiCom 2020)
Reference: pp. 718-731, 2020
Authors: Carlos Bocanegra Guerra, NEC Laboratories America, Inc., Northeastern University; Mohammad A. Khojastepour, NEC Laboratories America, Inc.; Mustafa Y. Arslan, NEC Laboratories America, Inc.; Eugene Chai, NEC Laboratories America, Inc.; Sampath Rangarajan, NEC Laboratories America, Inc.; Kaushik R. Chowdhury, Northeastern University
Abstract: Retailers are aiming to enhance customer experience by automating the checkout process. The key impediment here is the effort to manually align the product barcode with the scanner, requiring sequential handling of items without blocking the line-of-sight of the laser beam. While recent systems such as Amazon Go eliminate human involvement using an extensive array of cameras, we propose a privacy-preserving alternative, RFGo, that identifies products using passive RFID tags. Foregoing continuous monitoring of customers throughout the store, RFGo scans the products in a dedicated checkout area that is large enough for customers to simply walk in and stand until the scan is complete (in two seconds). Achieving such low-latency checkout is not possible with traditional RFID readers, which decode tags using one antenna at a time. To overcome this, RFGo includes a custom-built RFID reader that simultaneously decodes a tag’s response from multiple carrier-level synchronized antennas enabling a large set of tag observations in a very short time. RFGo then feeds these observations to a neural network that accurately distinguishes the products within the checkout area from those that are outside. We build a prototype of RFGo and evaluate its performance in challenging scenarios. Our experiments show that RFGo is extremely accurate, fast and well-suited for practical deployment in apparel stores.
Publication Link: https://dl.acm.org/doi/10.1145/3372224.3419211