Video based wildfire detection at night

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage868en_US
dc.citation.issueNumber6en_US
dc.citation.spage860en_US
dc.citation.volumeNumber44en_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.accessioned2016-02-08T10:03:08Z
dc.date.available2016-02-08T10:03:08Z
dc.date.issued2009-05-06en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThere has been an increasing interest in the study of video based fire detection algorithms as video based surveillance systems become widely available for indoor and outdoor monitoring applications. A novel method explicitly developed for video based detection of wildfires at night (in the dark) is presented in this paper. The method comprises four sub-algorithms: (i) slow moving video object detection, (ii) bright region detection, (iii) detection of objects exhibiting periodic motion, and (iv) a sub-algorithm interpreting the motion of moving regions in video. Each of these sub-algorithms characterizes an aspect of fire captured at night by a visible range PTZ camera. Individual decisions of the sub-algorithms are combined together using a least-mean-square (LMS) based decision fusion approach, and fire/nofire decision is reached by an active learning method.en_US
dc.identifier.doi10.1016/j.firesaf.2009.04.003en_US
dc.identifier.issn0379-7112
dc.identifier.urihttp://hdl.handle.net/11693/22667
dc.language.isoEnglishen_US
dc.publisherELSEVIERen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.firesaf.2009.04.003en_US
dc.source.titleFire Safety Journalen_US
dc.subjectActive learningen_US
dc.subjectComputer visionen_US
dc.subjectDecision fusionen_US
dc.subjectFire detectionen_US
dc.subjectLeast-mean-square methodsen_US
dc.subjectOn-line learningen_US
dc.titleVideo based wildfire detection at nighten_US
dc.typeArticleen_US

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