An intelligent self-checkout system for smart retail

Bing-Fei Wu*, Wan Ju Tseng, Yung Shin Chen, Shih Jhe Yao, Po Ju Chang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2016 IEEE International Conference on System Science and Engineering, ICSSE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467389662
DOIs
StatePublished - 24 Aug 2016
Event2016 IEEE International Conference on System Science and Engineering, ICSSE 2016 - Puli, Taiwan
Duration: 7 Jul 20169 Jul 2016

Publication series

Name2016 IEEE International Conference on System Science and Engineering, ICSSE 2016

Conference

Conference2016 IEEE International Conference on System Science and Engineering, ICSSE 2016
CountryTaiwan
CityPuli
Period7/07/169/07/16

Keywords

  • Data mining
  • Deep learning
  • Multi-object detection
  • Self-checkout
  • Smart retail

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