QUT ePrints

Identifying customer behaviour and dwell time using soft biometrics

Denman, Simon, Bialkowski, Alina, Fookes, Clinton B., Sridharan, Sridha, Xiang , T., & Gong, S. (2012) Identifying customer behaviour and dwell time using soft biometrics. In Shan, C., Porikli , F., Xiang , T., & Gong, S. (Eds.) Video Analytics for Business Intelligence [Studies in Computational Intelligence, Volume 409]. Springer, Germany, pp. 199-238.

View at publisher

Abstract

In a commercial environment, it is advantageous to know how long it takes customers to move between different regions, how long they spend in each region, and where they are likely to go as they move from one location to another. Presently, these measures can only be determined manually, or through the use of hardware tags (i.e. RFID). Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. They include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. While these traits cannot provide robust authentication, they can be used to provide identification at long range, and aid in object tracking and detection in disjoint camera networks. In this chapter we propose using colour, height and luggage soft biometrics to determine operational statistics relating to how people move through a space. A novel average soft biometric is used to locate people who look distinct, and these people are then detected at various locations within a disjoint camera network to gradually obtain operational statistics

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

125 since deposited on 21 Jun 2012
48 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 50990
Item Type: Book Chapter
Keywords: Soft Biometrics, Operational Analytics, Person Re-detection
DOI: 10.1007/978-3-642-28598-1_7
ISBN: 978-3-642-28597-4
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Springer Berlin Heidelberg.
Copyright Statement: The original publication is available at SpringerLink http://www.springerlink.com
Deposited On: 21 Jun 2012 12:50
Last Modified: 26 Oct 2012 11:49

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page