A context-based approach for detecting suspicious behaviours

Wiliem, Arnold, Madasu, Vamsi, Boles, Wageeh and Yarlagadda, Prasad (2009). A context-based approach for detecting suspicious behaviours. In: , Proceedings 2009 Digital Image Computing:Techniques and Applications DICTA 2009. DICTA 2009: Digital Image Computing:Techniques and Applications, Melbourne, VIC, Australia, (146-153). 1-3 December 2009.


Author Wiliem, Arnold
Madasu, Vamsi
Boles, Wageeh
Yarlagadda, Prasad
Title of paper A context-based approach for detecting suspicious behaviours
Conference name DICTA 2009: Digital Image Computing:Techniques and Applications
Conference location Melbourne, VIC, Australia
Conference dates 1-3 December 2009
Convener Australian Pattern Recognition Society (APRS)
Proceedings title Proceedings 2009 Digital Image Computing:Techniques and Applications DICTA 2009
Journal name 2009 Digital Image Computing: Techniques and Applications (dicta 2009)
Place of Publication Piscataway, NJ, U.S.A.
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Publication Year 2009
Sub-type Fully published paper
DOI 10.1109/DICTA.2009.31
ISBN 978-0-7695-3866-2
0-7695-3866-5
Start page 146
End page 153
Total pages 8
Language eng
Formatted Abstract/Summary A video surveillance system capable of detecting suspicious activities or behaviours is of paramount importance to law enforcement agencies. Such a system will not only reduce the work load of security personnel involved with monitoring the CCTV video feeds but also improve the time required to respond to any incident. There are two well known models to detect suspicious behaviour: misuse detection models which are dependent on suspicious behaviour definitions and anomaly detection models which measure deviations from defined normal behaviour. However, it is nearly possible to encapsulate the entire spectrum of either suspicious or normal behaviour. One of the ways to overcome this problem is by developing a system which learns in real time and adapts itself to behaviour which can be considered as common and normal or uncommon and suspicious. We present an approach utilising contextual information. Two contextual features, namely, type of behaviour and the commonality level of each type are extracted from longterm observation. Then, a data stream model which treats the incoming data as a continuous stream of information is used to extract these features. We further propose a clustering algorithm which works in conjunction with data stream model. Experiments and comparisons are conducted on the well known CAVIAR datasets to show the efficacy of utilising contextual information for detecting suspicious behaviour. The proposed approach is generic in nature and can be applicable to any features. However for the purpose of this study, we have employed pedestrian trajectories to represent the behaviour of people.
© 2009 IEEE.
Subjects 0801 Artificial Intelligence and Image Processing
Keyword Context
Human behaviour anomaly detection
Smart video surveillance
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Presented during Session 2A "Surveillance, Defence and Industrial Applications" as paper no. 128.

 
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Created: Mon, 15 Nov 2010, 13:50:32 EST by Jon Swabey on behalf of Faculty Of Engineering, Architecture & Info Tech