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.
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
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