Li, Z., Mihaylova, L.S. orcid.org/0000-0001-5856-2223, Isupova, O. et al. (1 more author) (2017) Autonomous Flame Detection in Videos with a Dirichlet Process Gaussian Mixture Color Model. IEEE Transactions on Industrial Informatics, 14 (3). pp. 1146-1154. ISSN 1551-3203
Abstract
This paper proposes a flame detection framework based on the color, dynamics and flickering properties of flames. The distribution of flame colors is modelled by a Gaussian Mixture Model whose number of Gaussian component is estimated by a Dirichlet process from training data rather than set empirically. The proposed approach estimates the flame color distribution more accurately as it can determine the number of Gaussian components of the mixture model automatically. Additionally, a probabilistic saliency analysis method and a one-dimensional wavelet transform are used to extract motion saliency and filtered temporal series as features, describing the dynamics and flickering properties of flames. The developed Dirichlet Process Gaussian Mixture Model based approach for autonomous flame detection is tested on various videos and achieves frame-wise accuracy higher than 95%.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. This is an author produced version of a paper subsequently published in IEEE Transactions on Industrial Informatics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Flame detection; Dirichlet Process Gaussian mixture model; saliency analysis |
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: | 30 Oct 2017 12:19 |
Last Modified: | 15 Jul 2020 11:10 |
Published Version: | https://doi.org/10.1109/TII.2017.2768530 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/TII.2017.2768530 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:123156 |