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http://dx.doi.org/10.9717/kmms.2015.18.10.1197

Smoke Detection Method Using Local Binary Pattern Variance in RGB Contrast Imag  

Kim, Jung Han (Dept. of Electrical, Electronics & Information Communications Eng., Graduate School, Tongmyong University)
Bae, Sung-Ho (Dept. of Biomedical Eng., Tongmyong UniversityDept. of Biomedical Eng., Tongmyong University)
Publication Information
Journal of Korea Multimedia Society / v.18, no.10, 2015 , pp. 1197-1204 More about this Journal
Abstract
Smoke detection plays an important role for the early detection of fire. In this paper, we suggest a newly developed method that generated LBPV(Local Binary Pattern Variance)s as special feature vectors from RGB contrast images can be applied to detect smoke using SVM(Support Vector Machine). The proposed method rearranges mean value of the block from each R, G, B channel and its intensity of the mean value. Additionally, it generates RGB contrast image which indicates each RGB channel’s contrast via smoke’s achromatic color. Uniform LBPV, Rotation-Invariance LBPV, Rotation-Invariance Uniform LBPV are applied to RGB Contrast images so that it could generate feature vector from the form of LBP. It helps to distinguish between smoke and non smoke area through SVM. Experimental results show that true positive detection rate is similar but false positive detection rate has been improved, although the proposed method reduced numbers of feature vector in half comparing with the existing method with LBP and LBPV.
Keywords
Smoke Detection; Local Binary Pattern Variance; RGB Contrast Image;
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Times Cited By KSCI : 1  (Citation Analysis)
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