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Fire Detection Approach using Robust Moving-Region Detection and Effective Texture Features of Fire

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2013, 18(6), pp.21-28
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Truc Kim Thi Nguyen 1,  강명수 1,  Cheol Hong Kim 2,  Jong Myon Kim 1

1울산대학교
2전남대학교

Accredited

ABSTRACT

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

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This paper was written with support from the National Research Foundation of Korea.
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