Isupova, O., Kuzin, D. orcid.org/0000-0003-3582-722X and Mihaylova, L. (2016) Anomaly detection in video with Bayesian nonparametrics. In: ICML2016 Anomaly Detection Workshop, June 24th, 2016, New York, USA.
Abstract
A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper. Batch and online Gibbs samplers are developed for inference. The paper introduces a new abnormality measure for decision making. The proposed method is evaluated on both synthetic and real data. The comparison with a non-dynamic model shows the superiority of the proposed dynamic one in terms of the classification performance for anomaly detection.
Metadata
Item Type: | Conference or Workshop Item |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 The Author(s) |
Keywords: | stat.ML; stat.ML |
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 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/K021516/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Jul 2017 09:55 |
Last Modified: | 29 Mar 2018 03:35 |
Published Version: | https://drive.google.com/file/d/0B8Dg3PBX90KNYjdWN... |
Status: | Published |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:114723 |