Isupova, O., Kuzin, D. and Mihaylova, L. (2015) Abnormal behaviour detection in video using topic modeling. In: USES Conference Proceedings. University of Sheffield Engineering Symposium - USES 2015, 2015-06-24., Sheffield, UK. The University of Sheffield
Abstract
The growth of the number of surveillance systems makes it is impossible to process data by human operators thereby autonomous algorithms are required in a decision-making procedure. A novel dynamic topic modeling approach for abnormal behaviour detection in video is proposed. Activities and behaviours in the scene are described by the topic model where temporal dynamics for behaviours is assumed. Here we implement Expectation-Maximisation algorithm for inference in the model and show in the experiments that it outperforms the Gibbs sampling inference scheme that is originally proposed in [1].
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 The Authors. For re-use permissions, please contact the authors. |
Keywords: | Abnormal behaviour; computer vision; Expectation-Maximisation algorithm; Gibbs sampling; topic modeling |
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) |
Funding Information: | Funder Grant number European Commission - FP6/FP7 TRAX - 607400 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Apr 2016 11:27 |
Last Modified: | 18 Jun 2020 09:15 |
Status: | Published |
Publisher: | The University of Sheffield |
Refereed: | Yes |
Identification Number: | 10.15445/02012015.18 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:97550 |