摘要
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.
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.
| 原文 | English |
|---|---|
| 主出版物標題 | 2020 IEEE Wireless Communications and Networking Conference (IEEE WCNC 2020) |
| 發行者 | Institute of Electrical and Electronics Engineers Inc. |
| 頁數 | 6 |
| DOIs | |
| 出版狀態 | Published - 25 5月 2020 |