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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Video based wildfire detection at night

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      Author
      Günay, O.
      Taşdemir K.
      Töreyin, B. U.
      Çetin, A. Enis
      Date
      2009-05-06
      Source Title
      Fire Safety Journal
      Print ISSN
      0379-7112
      Publisher
      ELSEVIER
      Volume
      44
      Issue
      6
      Pages
      860 - 868
      Language
      English
      Type
      Article
      Item Usage Stats
      107
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      185
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      Abstract
      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 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.
      Keywords
      Active learning
      Computer vision
      Decision fusion
      Fire detection
      Least-mean-square methods
      On-line learning
      Permalink
      http://hdl.handle.net/11693/22667
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.firesaf.2009.04.003
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      • Department of Electrical and Electronics Engineering 3524
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