Detecting commonly occupied regions in video sequences
Wiliem, Arnold, Madasu, Vamsi K., Wageeh, Boles, & Yarlagadda, Prasad K. (2008) Detecting commonly occupied regions in video sequences. In TENCON 2008, IEEE Region 10 Conference, November 18-21, 2008, University of Hyderabad, Hyderabad, India.
An effective video surveillance system relies on the detection of suspicious activities. In recent times, there has been an increasing focus on detecting anomalies in human behaviour using surveillance cameras as they provide a clue to preventing breaches in security. Human behaviour can be termed as suspicious when it is uncommon in occurrence and deviates from commonly understood behaviour within a particular context. This work aims to detect regions of interest in video sequences based on an understanding of uncommon behaviour. A commonality value is calculated to distinguish between common and uncommon occurrences. The proposed strategy is validated by classifying commonly occupied walking path regions in a shopping mall corridor and CAVIAR database is used for this purpose. The results demonstrate the efficacy of the proposed approach in detecting deviant walking paths.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||behaviour analyses, 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 2008 IEEE|
|Copyright Statement:||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:||16 Feb 2009 10:20|
|Last Modified:||29 Feb 2012 23:47|
Repository Staff Only: item control page