Video based wildfire detection at night
Author
Günay, O.
Taşdemir K.
Töreyin, B. U.
Çetin, A. Enis
Date
2009-05-06Source Title
Fire Safety Journal
Print ISSN
0379-7112
Publisher
ELSEVIER
Volume
44
Issue
6
Pages
860 - 868
Language
English
Type
ArticleItem 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 learningComputer vision
Decision fusion
Fire detection
Least-mean-square methods
On-line learning