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
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: |
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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 | ||||||
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