Isupova, O., Mihaylova, L., Kuzin, D. et al. (2 more authors) (2015) An expectation maximisation algorithm for behaviour analysis in video. In: 18th International Conference on Information Fusion (Fusion), 2015. 18th International Conference on Information Fusion, 06-09 Jul 2015, Washington D.C (USA). IEEE , 126 - 133. ISBN 978-0-9824-4386-6
Abstract
Surveillance systems require advanced algorithms able to make decisions without a human operator or with minimal assistance from human operators. In this paper we propose a novel approach for dynamic topic modeling to detect abnormal behaviour in video sequences. The topic model describes activities and behaviours in the scene assuming behaviour temporal dynamics. The new inference scheme based on an Expectation-Maximisation algorithm is implemented without an approximation at intermediate stages. The proposed approach for behaviour analysis is compared with a Gibbs sampling inference scheme. The experiments both on synthetic and real data show that the model, based on Expectation-Maximisation approach, outperforms the one, based on Gibbs sampling scheme.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Copyright 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Nov 2015 14:38 |
Last Modified: | 19 Dec 2022 13:32 |
Published Version: | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?... |
Status: | Published |
Publisher: | IEEE |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:91449 |