Material Identification Via RFID For Smart Shopping
David Wang, Derek Goh, Jiale Zhang
TL;DR
This work introduces an RFID-based material-sensing approach to strengthen theft prevention in cashierless retail by classifying the container surrounding an item using RSSI and phase-angle features. A neural network leverages four RFID-derived features, with and without distance information, to identify seven container classes in a simulated store; one-second samples achieve about 89% accuracy, while distance-aware inputs maintain robust performance up to 2 meters. The method demonstrates real-time potential using existing RFID infrastructure to flag suspicious items at choke points for camera checks or staff intervention. Overall, the study provides a practical, low-cost loss-prevention tool that can enhance item-to-customer association in cashierless environments, with future work needed for live-store validation and occlusion handling.
Abstract
Cashierless stores rely on computer vision and RFID tags to associate shoppers with items, but concealed items placed in backpacks, pockets, or bags create challenges for theft prevention. We introduce a system that turns existing RFID tagged items into material sensors by exploiting how different containers attenuate and scatter RF signals. Using RSSI and phase angle, we trained a neural network to classify seven common containers. In a simulated retail environment, the model achieves 89% accuracy with one second samples and 74% accuracy from single reads. Incorporating distance measurements, our system achieves 82% accuracy across 0.3-2m tag to reader separations. When deployed at aisle or doorway choke points, the system can flag suspicious events in real time, prompting camera screening or staff intervention. By combining material identification with computer vision tracking, our system provides proactive loss prevention for cashierless retail while utilizing existing infrastructure.