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

• Published in: Journal of the Korea Society of Computer and Information Vol. 18; no. 6; pp. 21 - 28
• Main Authors: 트룩 뉘엔(Truc Kim Thi Nguyen), 강명수(Myeongsu Kang), 김철홍(Cheol-Hong Kim), 김종면(Jong-Myon Kim)
• Format: Journal Article
• Language: English
• Published: 한국컴퓨터정보학회 28.06.2013
• Subjects: 컴퓨터학; 움직임 영역 감지; Fire detection; 그레이 레벨 동시발생 행렬; Moving region detection; Grey level co-occurrence matrix; 화재 감지; 서포터 벡터 머신; Support vector machine
• ISSN: 1598-849X; 2383-9945
• DOI: 10.9708/jksci.2013.18.6.021

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. KCI Citation Count: 0