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A new PSO-based approach to fire flame detection using K-Medoids clustering
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posted on 2017-02-01, 00:00 authored by Seyedamin Khatami, S Mirghasemi, Abbas KhosraviAbbas Khosravi, Chee Peng LimChee Peng Lim, Saeid NahavandiAutomated computer vision-based fire detection has gained popularity in recent years, as every fire detection needs to be fast and accurate. In this paper, a new fire detection method using image processing techniques is proposed. We explore how to create a fire flame-based colour space via a linear multiplication of a conversion matrix and colour features of a sample image. We show how the matrix multiplication can result in a differentiating colour space, in which the fire part is highlighted and the non-fire part is dimmed. Particle Swarm Optimization (PSO) and sample pixels from an image are used to obtain the weights of the colour-differentiating conversion matrix, and K-medoids provides a fitness metric for the PSO procedure. The obtained conversion matrix can be used for fire detection on different fire images without performing the PSO procedure. This allows a fast and easy implementable fire detection system. The empirical results indicate that the proposed method provides both qualitatively and quantitatively better results when compared to some of the conventional and state-of-the-art algorithms.
History
Journal
Expert systems with applicationsVolume
68Pagination
69 - 80Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0957-4174Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2016, ElsevierUsage metrics
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No categories selectedKeywords
Fire detectionParticle swarm optimisationK-medoidsOtsu’s thresholding methodContrast enhancementScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicOperations Research & Management ScienceComputer ScienceEngineeringOtsu's thresholding methodCOMPUTER VISIONVIDEOSENSOR
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