Locating people in video from semantic descriptions : a new database and approach

Halstead, Michael, Denman, Simon, Sridharan, Sridha, & Fookes, Clinton B. (2014) Locating people in video from semantic descriptions : a new database and approach. In Proceedings of the 22nd International Conference on Pattern Recognition, IEEE, Stockholm, Sweden, pp. 4501-4506.

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The location of previously unseen and unregistered individuals in complex camera networks from semantic descriptions is a time consuming and often inaccurate process carried out by human operators, or security staff on the ground. To promote the development and evaluation of automated semantic description based localisation systems, we present a new, publicly available, unconstrained 110 sequence database, collected from 6 stationary cameras. Each sequence contains detailed semantic information for a single search subject who appears in the clip (gender, age, height, build, hair and skin colour, clothing type, texture and colour), and between 21 and 290 frames for each clip are annotated with the target subject location (over 11,000 frames are annotated in total).

A novel approach for localising a person given a semantic query is also proposed and demonstrated on this database. The proposed approach incorporates clothing colour and type (for clothing worn below the waist), as well as height and build to detect people. A method to assess the quality of candidate regions, as well as a symmetry driven approach to aid in modelling clothing on the lower half of the body, is proposed within this approach. An evaluation on the proposed dataset shows that a relative improvement in localisation accuracy of up to 21 is achieved over the baseline technique.

Impact and interest:

3 citations in Scopus
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3 citations in Web of Science®

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ID Code: 72887
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: soft biometrics, database, surveillance, person search, localisation
DOI: 10.1109/ICPR.2014.770
ISBN: 978-1-4799-5208-3
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Please consult the authors
Deposited On: 18 Jun 2014 23:13
Last Modified: 02 Nov 2014 00:43

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