Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association Nguyen, Nam, Venkatesh, Svetha and Bui, Hung 2006, Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association, in BMVC 2006 : Proceedings of the 17th British Machine Vision Conference, British Machine Vision Association, [Edinburgh, Scotland], pp. 1239-1248. Attached Files Name Description MIMEType Size Downloads venkatesh-recognisingbehaviours-2006.pdf Published version application/pdf 156.12KB 256 Title Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association Author(s) Nguyen, Nam Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Bui, Hung Conference name British Machine Vision Conference (17th : 2006 : Edinburgh, Scotland) Conference location Edinburgh, Scotland Conference dates 4-7 Sep. 2006 Title of proceedings BMVC 2006 : Proceedings of the 17th British Machine Vision Conference Editor(s) Chantler, M. J. Trucco, E. Fisher, R. B. Publication date 2006 Conference series British Machine Vision Conference Start page 1239 End page 1248 Total pages 10 Publisher British Machine Vision Association Place of publication [Edinburgh, Scotland] Keyword(s) surveillance systems filters hidden Markov models (HHMM) joint probabilistic data associaon filters (JPDAF) Rao-Blackwellised particle filters (RBPF) Summary Recognising behaviours of multiple people, especially high-level behaviours, is an important task in surveillance systems. When the reliable assignment of people to the set of observations is unavailable, this task becomes complicated. To solve this task, we present an approach, in which the hierarchical hidden Markov model (HHMM) is used for modeling the behaviour of each person and the joint probabilistic data association filters (JPDAF) is applied for data association. The main contributions of this paper lie in the integration of multiple HHMMs for recognising high-level behaviours of multiple people and the construction of the Rao-Blackwellised particle filters (RBPF) for approximate inference. Preliminary experimental results in a real environment show the robustness of our integrated method in behaviour recognition and its advantage over the use of Kalman filter in tracking people. ISBN 1904410146 Language eng Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed Copyright notice ©2006, The Authors Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30044805 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Tue, 01 May 2012, 10:59:14 EST Thu, 03 May 2012, 16:08:02 EST Thu, 03 May 2012, 16:09:48 EST Wed, 09 May 2012, 09:58:58 EST Tue, 28 Jan 2014, 14:40:05 EST Sat, 17 Jun 2017, 03:10:54 EST Tue, 05 Dec 2017, 12:32:00 EST Fri, 07 Sep 2018, 15:55:35 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 32 times in Scopus Search Google Scholar Access Statistics: 647 Abstract Views, 256 File Downloads  -  Detailed Statistics Created: Tue, 01 May 2012, 10:59:14 EST

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