Learning semantic scene models from observing activity in visual surveillance

Makris, Dimitios and Ellis, Tim (2005) Learning semantic scene models from observing activity in visual surveillance. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 35(3), pp. 397-408. ISSN (print) 1083-4419

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This paper considers the problem of automatically learning an activity-based semantic scene model from a stream of video data. A scene model is proposed that labels regions according to an identifiable activity in each region, such as entry/exit zones, junctions, paths, and stop zones. We present several unsupervised methods that learn these scene elements and present results that show the efficiency of our approach. Finally, we describe how the models can be used to support the interpretation of moving objects in a visual surveillance environment.

Item Type: Article
Additional Information: This work was supported by the Engineering and Physical Sciences Research Council [grant number GR/M58030].
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Computing, Information Systems and Mathematics (until 2011)
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Depositing User: Sue Snelling
Date Deposited: 09 May 2007
Last Modified: 03 Jul 2014 09:59
DOI: https://doi.org/10.1109/TSMCB.2005.846652
URI: http://eprints.kingston.ac.uk/id/eprint/782

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