Automatic Self-checkout System Using SURF, Brute Force Matcher, and RFID for Payment Process Optimization at Supermarket

Abstract

A long queue is common at a conventional supermarket. It is caused by inability of cashier to handle the task. Cashier could have an inconsistent performance, untrained skill, and fatigue. In addition, odd price tags could engage unnecessary conversation in giving change. This resulted on the service provided to be longer. The current solution for this problem is Radio Frequency Identification (RFID) and debit card usage. As per our survey at local supermarket, the usage of RFID could extend cashier service by up to five minutes fifteen seconds for the purchase of six test goods, meanwhile the cash took up to three minutes 23 seconds. Intelligent self-checkout system has been proposed but requires a high-end graphics card. In this study, we propose an automatic self-checkout system based on Speeded Up Robust Features (SURF), brute force matcher, and RFID for payment process optimization at supermarket. The system consists of an image scanner, a conveyor set which moves the customer’s goods into scanning area. After the goods is detected, customer pay the displayed price using RFID card. The result of our experiment has 77.78% detection rate and 16 seconds of payment process. Compared to traditional