Video based wildfire detection at night


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

FIRE SAFETY JOURNAL, cilt.44, sa.6, ss.860-868, 2009 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 44 Sayı: 6
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.firesaf.2009.04.003
  • Dergi Adı: FIRE SAFETY JOURNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.860-868
  • Anahtar Kelimeler: Fire detection, Least-mean-square methods, Active learning, Decision fusion, On-line learning, Computer vision, REAL-TIME FIRE, FLAME DETECTION
  • İstanbul Teknik Üniversitesi Adresli: Hayır

Özet

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.