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Discovery of activity structures using the hierarchical hidden Markov model

Nguyen, Nam and Venkatesh, Svetha 2005, Discovery of activity structures using the hierarchical hidden Markov model, in BMCV 2005 : Proceedings of the British Machine Vision Conference, [British Machine Vision Association], [Oxford, U. K.].

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Title Discovery of activity structures using the hierarchical hidden Markov model
Author(s) Nguyen, Nam
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name British Machine Vision Conference (16th : 2005 : Oxford, U. K.)
Conference location Oxford, U. K.
Conference dates 5-8 Sep. 2005
Title of proceedings BMCV 2005 : Proceedings of the British Machine Vision Conference
Editor(s) Clocksin, W. F.
Fitzgibbon, A. W.
Torr, P. H. S.
Publication date 2005
Conference series British Machine Vision Conference
Total pages 10
Publisher [British Machine Vision Association]
Place of publication [Oxford, U. K.]
Keyword(s) hidden Markov model
algorithm
surveillance system
behaviour
hierarchical hidden Markov model
Summary In building a surveillance system for monitoring people behaviours, it is important to understand the typical patterns of people's movement in the environment. This task is difficult when dealing with high-level behaviours. The flat model such as the hidden Markov model (HMM) is inefficient in differentiating between signatures of such behaviours. This paper examines structure learning for high-level behaviours using the hierarchical hidden Markov model (HHMM).We propose a two-phase learning algorithm in which the parameters of the behaviours at low levels are estimated first and then the structures and parameters of the behaviours at high levels are learned from multi-camera training data. Our algorithm is then evaluated using data from a real environment, demonstrating the robustness of the learned structure in recognising people's behaviour.
ISBN 1901725294
9781901725292
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 ©2005, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044911

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.