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A probabilistic framework for tracking in wide-area environments

Bui, Hung H., Venkatesh, Svetha and West, Geoff 2000, A probabilistic framework for tracking in wide-area environments, in ICPR 2000 : Proceedings of the International Conference on Pattern Recognition, IEEE, Washington, D. C., pp. 702-705.

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Title A probabilistic framework for tracking in wide-area environments
Author(s) Bui, Hung H.
Venkatesh, Svetha
West, Geoff
Conference name International Conference on Pattern Recognition (15th : 2000 : Barcelona, Spain)
Conference location Barcelona, Spain
Conference dates 3-8 Sep. 2000
Title of proceedings ICPR 2000 : Proceedings of the International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2000
Conference series International Conference on Pattern Recognition
Start page 702
End page 705
Total pages 4
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) bayesian methods
computer science
hidden Markov models
space technology
state estimation
state-space methods
surveillance
training data
uncertainty
working environment noise
Summary Surveillance in wide-area spatial environments is characterised by complex spatial layouts, large state space, and the use of multiple cameras/sensors. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. This requirement is particularly suited to the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN). In this paper, we propose the use of LDPN as the integrated framework for tracking in wide-area environments. We illustrate, with the help of a synthetic tracking scenario, how the parameters of the LDPN can be estimated from training data, and then used to draw predictions and answer queries about unseen tracks at various levels of detail.
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.
ISBN 0769507506
ISSN 1051-4651
Language eng
Field of Research 109999 Technology not elsewhere classified
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2000, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044539

Document type: Conference Paper
Collections: School of Information Technology
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