Isupova, O., Kuzin, D. and Mihaylova, L.S. orcid.org/0000-0001-5856-2223 (2016) Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video. In: 2016 19th International Conference on Information Fusion (FUSION). International Conference on Information Fusion, 04-08 Jul 2016, Heidelberg, Germany. IEEE , Heidelberg, Germany , pp. 750-757. ISBN 9780996452748
Abstract
This paper proposes a novel dynamic Hierarchical Dirichlet Process topic model that considers the dependence between successive observations. Conventional posterior inference algorithms for this kind of models require processing of the whole data through several passes. It is computationally intractable for massive or sequential data. We design the batch and online inference, based on the Gibbs sampling, for our model. It allows to process sequential data, incrementally updating the model by a new observation. The model is applied to abnormal behaviour detection in video sequences. A new abnormality measure is proposed for decision making. The proposed method is compared with the method based on the non-dynamic Hierarchical Dirichlet Process, for which we also derive the online Gibbs sampler and the abnormality measure. The experimental results show that the consideration of the dynamics in a topic model improves the classification performance for abnormal behaviour detection.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. This is an author produced version of a paper subsequently published in 2016 19th International Conference on Information Fusion . Uploaded in accordance with the publisher's self-archiving policy. |
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: | 02 Jun 2016 13:59 |
Last Modified: | 22 Jul 2020 10:20 |
Published Version: | http://ieeexplore.ieee.org/document/7527962/ |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:100347 |