TagBooth: Deep shopping data acquisition powered by RFID tags

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

doi:10.1109/INFOCOM.2015.7218547

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 proceeding › Conference article published in proceeding or book › Academic research › peer-review

39 Citations (Scopus)

Abstract

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 language	English
Title of host publication	2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015
Publisher	IEEE
Pages	1670-1678
Number of pages	9
Volume	26
ISBN (Electronic)	9781479983810
DOIs	https://doi.org/10.1109/INFOCOM.2015.7218547
Publication status	Published - 21 Aug 2015
Externally published	Yes
Event	34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015 - Hong Kong, Hong Kong Duration: 26 Apr 2015 → 1 May 2015

Conference

Conference	34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015
Country/Territory	Hong Kong
City	Hong Kong
Period	26/04/15 → 1/05/15

Keywords

Action Recognition
Deep Shopping Data
Motion Detection
RFID
TagBooth

ASJC Scopus subject areas

General Computer Science
Electrical and Electronic Engineering

More information

10.1109/INFOCOM.2015.7218547

Cite this

@inproceedings{9565dfba4a574701bcb775692f818a4b,

title = "TagBooth: Deep shopping data acquisition powered by RFID tags",

abstract = "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.",

keywords = "Action Recognition, Deep Shopping Data, Motion Detection, RFID, TagBooth",

author = "Tianci Liu and Lei Yang and Li, {Xiang Yang} and Huaiyi Huang and Yunhao Liu",

year = "2015",

month = aug,

day = "21",

doi = "10.1109/INFOCOM.2015.7218547",

language = "English",

volume = "26",

pages = "1670--1678",

booktitle = "2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015",

publisher = "IEEE",

address = "United States",

note = "34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015 ; Conference date: 26-04-2015 Through 01-05-2015",

}

Liu, T, Yang, L, Li, XY, Huang, H & Liu, Y 2015, TagBooth: Deep shopping data acquisition powered by RFID tags. in 2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015. vol. 26, 7218547, IEEE, pp. 1670-1678, 34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015, Hong Kong, Hong Kong, 26/04/15. https://doi.org/10.1109/INFOCOM.2015.7218547

TY - GEN

T1 - TagBooth: Deep shopping data acquisition powered by RFID tags

AU - Liu, Tianci

AU - Yang, Lei

AU - Li, Xiang Yang

AU - Huang, Huaiyi

AU - Liu, Yunhao

PY - 2015/8/21

Y1 - 2015/8/21

N2 - 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.

AB - 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.

KW - Action Recognition

KW - Deep Shopping Data

KW - Motion Detection

KW - RFID

KW - TagBooth

UR - http://www.scopus.com/inward/record.url?scp=84954223859&partnerID=8YFLogxK

U2 - 10.1109/INFOCOM.2015.7218547

DO - 10.1109/INFOCOM.2015.7218547

M3 - Conference article published in proceeding or book

VL - 26

SP - 1670

EP - 1678

BT - 2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015

PB - IEEE

T2 - 34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015

Y2 - 26 April 2015 through 1 May 2015

ER -

TagBooth: Deep shopping data acquisition powered by RFID tags

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this