Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association

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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, Svetha 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 Open Access Collection


Link to Related Work
Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association (deposited 25-06-2017) British Machine Vision Association (BMVA) Publisher (deposited 08-05-2012)

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