An intelligent self-checkout system for smart retail

Most of current self-checkout systems rely on barcodes, RFID tags, or QR codes attached on items to distinguish products. This paper proposes an Intelligent Self-Checkout System (ISCOS) embedded with a single camera to detect multiple products without any labels in real-time performance. In addition...

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Bibliographic Details
Main Authors: Wu, Bing-Fei, Tseng, Wan-Ju, Chen, Yung-Shin, Yao, Shih-Jhe, Chang, Po-Ju
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Data mining
Deep learning
Learning
Machine learning
Markets
Multi-object detection
Radio frequency identification
Real time
Real-time systems
Recommendations
Self-checkout
Shape
Smart retail
Training
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Description
Summary:Most of current self-checkout systems rely on barcodes, RFID tags, or QR codes attached on items to distinguish products. This paper proposes an Intelligent Self-Checkout System (ISCOS) embedded with a single camera to detect multiple products without any labels in real-time performance. In addition, deep learning skill is applied to implement product detection, and data mining techniques construct the image database employed as training dataset. Product information gathered from a number of markets in Taiwan is utilized to make recommendation to customers. The bounding boxes are annotated by background subtraction with a fixed camera to avoid time-consuming process for each image. The contribution of this work is to combine deep learning and data mining approaches to real-time multi-object detection in image-based checkout system.
ISSN:2325-0925
DOI:10.1109/ICSSE.2016.7551621