Fire detection in video using LMS based active learning

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage577en_US
dc.citation.issueNumber3en_US
dc.citation.spage551en_US
dc.citation.volumeNumber46en_US
dc.contributor.authorGünay, O.en_US
dc.contributor.authorTaşdemir K.en_US
dc.contributor.authorTöreyin, B. U.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.date.accessioned2016-02-08T09:58:06Z
dc.date.available2016-02-08T09:58:06Z
dc.date.issued2009en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractIn this paper, a video based algorithm for fire and flame detection is developed. In addition to ordinary motion and color clues, flame flicker is distinguished from motion of flame colored moving objects using Markov models. Irregular nature of flame boundaries is detected by performing temporal wavelet analysis using Hidden Markov Models as well. Color variations in fire is detected by computing the spatial wavelet transform of moving fire-colored regions. Boundary of flames are represented in wavelet domain and irregular nature of the boundaries of fire regions is also used as an indication of the flame flicker. Decisions from sub-algorithms are linearly combined using an adaptive active fusion method. The main detection algorithm is composed of four sub-algorithms (i) detection of fire colored moving objects, (ii) temporal, and (iii) spatial wavelet analysis for flicker detection and (iv) contour analysis of fire colored region boundaries. Each algorithm yields a continuous decision value as a real number in the range [-1, 1] at every image frame of a video sequence. Decision values from sub-algorithms are fused using an adaptive algorithm in which weights are updated using the least mean square (LMS) method in the training (learning) stage.en_US
dc.identifier.doi10.1007/s10694-009-0106-8en_US
dc.identifier.issn0015-2684
dc.identifier.urihttp://hdl.handle.net/11693/22288
dc.language.isoEnglishen_US
dc.publisherSpringeren_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10694-009-0106-8en_US
dc.source.titleFire Technologyen_US
dc.subjectActive learningen_US
dc.subjectDecision Fusionen_US
dc.subjectFire detectionen_US
dc.subjectLeast-mean-square methodsen_US
dc.subjectOn-line learningen_US
dc.titleFire detection in video using LMS based active learningen_US
dc.typeArticleen_US

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