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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. Attached Files Name Description MIMEType Size Downloads venkatesh-aprobabilistic-2000.pdf Published version application/pdf 367.16KB 11 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 Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Fri, 20 Apr 2012, 11:30:52 EST Mon, 23 Apr 2012, 14:19:54 EST Mon, 23 Apr 2012, 14:23:21 EST Mon, 30 Apr 2012, 10:03:21 EST Tue, 01 May 2012, 14:27:41 EST Filtered Full Access Statistics: 47 Abstract Views, 11 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:30:52 EST

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.