venkatesh-recognisingbehaviours-2006.pdf (156.12 kB)
Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association
conference contribution
posted on 2006-01-01, 00:00 authored by N Nguyen, Svetha VenkateshSvetha Venkatesh, H BuiRecognising 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.
History
Event
British Machine Vision Conference (17th : 2006 : Edinburgh, Scotland)Pagination
1239 - 1248Publisher
British Machine Vision AssociationLocation
Edinburgh, ScotlandPlace of publication
[Edinburgh, Scotland]Start date
2006-09-04End date
2006-09-07ISBN-10
1904410146Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2006, The AuthorsEditor/Contributor(s)
M Chantler, E Trucco, R FisherTitle of proceedings
BMVC 2006 : Proceedings of the 17th British Machine Vision ConferenceUsage metrics
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