A suspicious behaviour detection using a context space model for smart surveillance systems

Wiliem, Arnold, Madasu, Vamsi, Boles, Wageeh and Yarlagadda, Prasad (2012) A suspicious behaviour detection using a context space model for smart surveillance systems. Computer Vision and Image Understanding, 116 2: 194-209. doi:10.1016/j.cviu.2011.10.001

Author Wiliem, Arnold
Madasu, Vamsi
Boles, Wageeh
Yarlagadda, Prasad
Title A suspicious behaviour detection using a context space model for smart surveillance systems
Journal name Computer Vision and Image Understanding   Check publisher's open access policy
ISSN 1077-3142
Publication date 2012-02-02
Year available 2012
Sub-type Article (original research)
DOI 10.1016/j.cviu.2011.10.001
Open Access Status Not Open Access
Volume 116
Issue 2
Start page 194
End page 209
Total pages 16
Place of publication Maryland Heights, MO United States
Publisher Academic Press
Language eng
Abstract Video surveillance systems using Closed Circuit Television (CCTV) cameras, is one of the fastest growing areas in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. This work attempts to address these problems by proposing an automatic suspicious behaviour detection which utilises contextual information. The utilisation of contextual information is done via three main components: a context space model, a data stream clustering algorithm, and an inference algorithm. The utilisation of contextual information is still limited in the domain of suspicious behaviour detection. Furthermore, it is nearly impossible to correctly understand human behaviour without considering the context where it is observed. This work presents experiments using video feeds taken from CAVIAR dataset and a camera mounted on one of the buildings Z-Block) at the Queensland University of Technology, Australia. From these experiments, it is shown that by exploiting contextual information, the proposed system is able to make more accurate detections, especially of those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information gives critical feedback to the system designers to refine the system.
Keyword Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 16 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 24 times in Scopus Article | Citations
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Created: Tue, 05 May 2015, 20:52:48 EST by Arnold Wiliem on behalf of School of Information Technol and Elec Engineering