A context-based approach for detecting suspicious behaviours
Wiliem, Arnold, Madasu, Vamsi K., Boles, Wageeh W., & Yarlagadda, Prasad K. (2009) A context-based approach for detecting suspicious behaviours. In Digital Image Computing: Techniques and Applications (DICTA) 2009, 1-3 December 2009, Australia, Victoria, Melbourne. (In Press)
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.
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|Item Type:||Conference Paper|
|Keywords:||behaviour analyses, contextual information, suspicious behaviour, anomaly detection, surveillance systems, security|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2009 IEEE|
|Copyright Statement:||©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||02 Sep 2009 07:47|
|Last Modified:||01 Mar 2012 00:02|
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