Fire detection in video using LMS based active learning

Osman Günay*, Kasim Taşdemir, B. Uǧur Töreyin, A. Enis Çetin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)551-577
Number of pages27
JournalFire Technology
Issue number3
Publication statusPublished - Jul 2010
Externally publishedYes


This work was supported in part by the Scientific and Technical Research Council of Turkey, TUBITAK, with grant no. 106G126 and 105E191, and in part by European Commission 6th Framework Program with grant number FP6-507752 (MUSCLE Network of Excellence Project).

FundersFunder number
European Commission 6th Framework ProgramFP6-507752
TUBITAK106G126, 105E191
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu


    • Active learning
    • Decision Fusion
    • Fire detection
    • Least-mean-square methods
    • On-line learning


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