Dergi makalesi Açık Erişim
Gunay, Osman; Tasdemir, Kasim; Toreyin, B. Ugur; Cetin, A. Enis
{ "@context": "https://schema.org/", "@id": 42017, "@type": "ScholarlyArticle", "creator": [ { "@type": "Person", "affiliation": "Bilkent Univ, Dept Elect & Elect Engn, TR-06800 Ankara, Turkey", "name": "Gunay, Osman" }, { "@type": "Person", "affiliation": "Bilkent Univ, Dept Elect & Elect Engn, TR-06800 Ankara, Turkey", "name": "Tasdemir, Kasim" }, { "@type": "Person", "affiliation": "Bilkent Univ, Dept Elect & Elect Engn, TR-06800 Ankara, Turkey", "name": "Toreyin, B. Ugur" }, { "@type": "Person", "affiliation": "Bilkent Univ, Dept Elect & Elect Engn, TR-06800 Ankara, Turkey", "name": "Cetin, A. Enis" } ], "datePublished": "2009-01-01", "description": "There 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 M 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. (C) 2009 Elsevier Ltd. All rights reserved.", "headline": "Video based wildfire detection at night", "identifier": 42017, "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", "license": "http://www.opendefinition.org/licenses/cc-by", "name": "Video based wildfire detection at night", "url": "https://aperta.ulakbim.gov.tr/record/42017" }
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