venkatesh-hierarchicalmonitoring-2002.pdf (298.34 kB)
Hierarchical monitoring of people's behaviors in complex environments using multiple cameras
conference contribution
posted on 2002-01-01, 00:00 authored by N Nguyen, Svetha VenkateshSvetha Venkatesh, G West, H BuiWe 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.
History
Event
International Conference on Pattern Recognition (16th : 2002 : Quebec, Canada)Pagination
13 - 16Publisher
IEEELocation
Quebec, CanadaPlace of publication
Los Alamitos, Calif.Publisher DOI
Start date
2002-08-11End date
2002-08-15ISSN
1051-4651Language
engNotes
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.Publication classification
E1.1 Full written paper - refereedCopyright notice
2002, IEEETitle of proceedings
ICPR 2002 : Proceedings of the 16th International Conference on Pattern RecognitionUsage metrics
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