Video based wildfire detection at night

• Published in: Fire safety journal Vol. 44; no. 6; pp. 860 - 868
• Main Authors: Gunay, O, Tasdemir, K, Ugur Toreyin, B, Enis Cetin, A
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.08.2009; Elsevier
• Subjects: Active learning; Applied sciences; Biological and medical sciences; Building technical equipments; Buildings; Buildings. Public works; Computer vision; Decision fusion; Detection. Firefighting. Smoke control. Evacuation; Exact sciences and technology; Fire detection; Fire protection; Fundamental and applied biological sciences. Psychology; Least-mean-square methods; On-line learning; Phytopathology. Animal pests. Plant and forest protection; Weather damages. Fires; Computer vision; Least-mean-square methods; Fire detection; Active learning; Decision fusion; On-line learning; Forests; Night; Video technique; Algorithm; Learning; Wildfire; Experimental result; Least squares method
• ISSN: 0379-7112; 1873-7226
• DOI: 10.1016/j.firesaf.2009.04.003

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.