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.

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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

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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
Deposited On: 21 Jun 2012 02:50
Last Modified: 21 Jun 2015 12:23

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