Smart Shopping Carts Based on Mobile Computing and Deep Learning Cloud Services
| Autor: | Muhammad Atif Sarwar, Hong-Chuan Chi, Kuan-Wen Liu, Yih-Lang Li, Tsi Ui Ik, Yousef-Awwad Daraghmi |
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| Rok vydání: | 2020 |
| Předmět: |
Database
Computer science business.industry Event (computing) Deep learning 010401 analytical chemistry Process (computing) Mobile computing Cloud computing 02 engineering and technology computer.software_genre 01 natural sciences 0104 chemical sciences Mobile cloud computing Index (publishing) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | WCNC |
| DOI: | 10.1109/wcnc45663.2020.9120574 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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