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 contributionpeer-review

14 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
Country/TerritoryTaiwan
CityPuli
Period7/07/169/07/16

Keywords

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

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