Fire Detection in Video Using LMS Based Active Learning

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

FIRE TECHNOLOGY, vol.46, no.3, pp.551-577, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 3
  • Publication Date: 2010
  • Doi Number: 10.1007/s10694-009-0106-8
  • Journal Name: FIRE TECHNOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.551-577
  • Keywords: Fire detection, Least-mean-square methods, Active learning, Decision Fusion, On-line learning, REAL-TIME FIRE, FLAME DETECTION
  • Istanbul Technical University Affiliated: No


In this paper, a video based algorithm for fire and flame detection is developed. In addition to ordinary motion and color clues, flame flicker is distinguished from motion of flame colored moving objects using Markov models. Irregular nature of flame boundaries is detected by performing temporal wavelet analysis using Hidden Markov Models as well. Color variations in fire is detected by computing the spatial wavelet transform of moving fire-colored regions. Boundary of flames are represented in wavelet domain and irregular nature of the boundaries of fire regions is also used as an indication of the flame flicker. Decisions from sub-algorithms are linearly combined using an adaptive active fusion method. The main detection algorithm is composed of four sub-algorithms (i) detection of fire colored moving objects, (ii) temporal, and (iii) spatial wavelet analysis for flicker detection and (iv) contour analysis of fire colored region boundaries. Each algorithm yields a continuous decision value as a real number in the range [-1, 1] at every image frame of a video sequence. Decision values from sub-algorithms are fused using an adaptive algorithm in which weights are updated using the least mean square (LMS) method in the training (learning) stage.