Günay, O.Taşdemir K.Töreyin, B. U.Çetin, A. Enis2016-02-082016-02-082009-05-060379-7112http://hdl.handle.net/11693/22667There 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.EnglishActive learningComputer visionDecision fusionFire detectionLeast-mean-square methodsOn-line learningArticle10.1016/j.firesaf.2009.04.003