Smart Shopping Carts Based on Mobile Computing and Deep Learning Cloud Services

• Published in: 2020 IEEE Wireless Communications and Networking Conference (WCNC) pp. 1 - 6
• Main Authors: Sarwar, Muhammad Atif, Daraghmi, Yousef-Awwad, Liu, Kuan-Wen, Chi, Hong-Chuan, Ik, Tsi -Ui, Li, Yih-Lang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2020
• Subjects: action segmentation; Cloud computing; Costs; Deep learning; Event detection; frame classification; iCart; just walk out technology; self-checkout; Sensor systems; shopping event detection; smart shopping cart; Streaming media; Training; YOLOv2
• ISSN: 1558-2612
• DOI: 10.1109/WCNC45663.2020.9120574

Self-checkout systems enable retailers to reduce costs and customers to process their purchases quickly without waiting in queues. However, existing self-checkout systems suffer from design problems as they require large hardware consisting of a camera, sensors, RFID and other IoT technologies which increases the cost of such systems. Therefore, we propose a smart shopping cart with self-checkout, called iCart, to improve customer's experience at retail stores by enabling just walk out checkout and overcome the aforementioned problems. iCart is based on mobile cloud computing and deep learning cloud services. In iCart, a checkout event video is captured and sent to the cloud server for classification and segmentation where an item is identified and added to the shopping list. The Linux based cloud server contained the yolov2 deep learning network. iCart is a lightweight system of low cost solution which is suitable for the small-scale retail stores. The system is evaluated using real-world checkout video, and the accuracy of the shopping event detection and item recognition is about 97%. iCart demo can be found at URL: http://nol.cs.nctu.edu.tw/iCart/index.html.