Autonomous flame detection in video based on saliency analysis and optical flow

Zhenglin Li, Olga Isupova, Lyudmila Mihaylova, Lucile Rossi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The paper proposes a flame detection method based on saliency analysis, optical flow estimation and temporal wavelet transform. Two separate saliency maps are first obtained based on the grayscale values and optical flow magnitudes of each frame using a saliency detector. Subsequently, the two maps are combined to extract candidate flame regions. To further discard falsely detected pixels, a colour model of flames and temporal wavelet transform are employed. The proposed algorithms can be applied in the autonomous and semi-autonomous systems for environmental surveillance and can reduce the load of human operators. Experiments illustrate the introduced method achieves around 91% true positive rate and 97% true negative rate.

Original languageEnglish
Title of host publication2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016
PublisherIEEE
Pages218-223
Number of pages6
ISBN (Electronic)978-1-4673-9708-7
ISBN (Print)978-1-4673-9709-4
DOIs
Publication statusPublished - 13 Feb 2017
Event2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016 - Baden-Baden, Germany
Duration: 19 Sep 201621 Sep 2016

Publication series

NameIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Volume0

Conference

Conference2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016
CountryGermany
CityBaden-Baden
Period19/09/1621/09/16

Cite this

Li, Z., Isupova, O., Mihaylova, L., & Rossi, L. (2017). Autonomous flame detection in video based on saliency analysis and optical flow. In 2016 IEEE lnternational Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2016 (pp. 218-223). (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems; Vol. 0). IEEE. https://doi.org/10.1109/MFI.2016.7849492