Video based wildfire detection at night


Gunay O., Tasdemir K., Toreyin B. U., Cetin A. E.

FIRE SAFETY JOURNAL, vol.44, no.6, pp.860-868, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 44 Issue: 6
  • Publication Date: 2009
  • Doi Number: 10.1016/j.firesaf.2009.04.003
  • Journal Name: FIRE SAFETY JOURNAL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.860-868
  • Keywords: Fire detection, Least-mean-square methods, Active learning, Decision fusion, On-line learning, Computer vision, REAL-TIME FIRE, FLAME DETECTION
  • Istanbul Technical University Affiliated: No

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 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.