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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.

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Title Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association
Author(s) Nguyen, Nam
Venkatesh, SvethaORCID iD for 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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044805

Document type: Conference Paper
Collections: School of Information Technology
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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.