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Hierarchical monitoring of people's behaviors in complex environments using multiple cameras

Nguyen, Nam T., Venkatesh, Svetha, West, Geoff and Bui, Hung H. 2002, Hierarchical monitoring of people's behaviors in complex environments using multiple cameras, in ICPR 2002 : Proceedings of the 16th International Conference on Pattern Recognition, IEEE, Los Alamitos, Calif., pp. 13-16, doi: 10.1109/ICPR.2002.1044577.

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Title Hierarchical monitoring of people's behaviors in complex environments using multiple cameras
Author(s) Nguyen, Nam T.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
West, Geoff
Bui, Hung H.
Conference name International Conference on Pattern Recognition (16th : 2002 : Quebec, Canada)
Conference location Quebec, Canada
Conference dates 11-15 Aug. 2002
Title of proceedings ICPR 2002 : Proceedings of the 16th International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2002
Conference series International Conference on Pattern Recognition
Start page 13
End page 16
Total pages 4
Publisher IEEE
Place of publication Los Alamitos, Calif.
Keyword(s) cameras
hierarchical systems
markov processes
probability distributions
public policy
Summary We present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people, we propose the use of the Abstract Hidden Markov Model (AHMM), which can be considered as an extension of the Hidden Markov Model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackwellised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. The results of the system demonstrate the ability to answer queries about people's behaviors at different levels of details using multiple cameras in a large and complex indoor environment.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISSN 1051-4651
Language eng
DOI 10.1109/ICPR.2002.1044577
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 ©2002, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044649

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.