TagBooth: Deep shopping data acquisition powered by RFID tags

Tianci Liu, Lei Yang, Xiang Yang Li, Huaiyi Huang, Yunhao Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

38 Citations (Scopus)


To stay competitive, plenty of data mining techniques have been introduced to help stores better understand consumers' behaviors. However, these studies are generally confined within the customer transaction data. Actually, another kind of 'deep shopping data', e.g. which and why goods receiving much attention are not purchased, offers much more valuable information to boost the product design. Unfortunately, these data are totally ignored in legacy systems. This paper introduces an innovative system, called TagBooth, to detect commodities' motion and further discover customers' behaviors, using COTS RFID devices. We first exploit the motion of tagged commodities by leveraging physical-layer information, like phase and RSS, and then design a comprehensive solution to recognize customers' actions. The system has been tested extensively in the lab environment and used for half a year in real retail store. As a result, TagBooth generally performs well to acquire deep shopping data with high accuracy.
Original languageEnglish
Title of host publication2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015
Number of pages9
ISBN (Electronic)9781479983810
Publication statusPublished - 21 Aug 2015
Externally publishedYes
Event34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015 - Hong Kong, Hong Kong
Duration: 26 Apr 20151 May 2015


Conference34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015
Country/TerritoryHong Kong
CityHong Kong


  • Action Recognition
  • Deep Shopping Data
  • Motion Detection
  • RFID
  • TagBooth

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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