@article{ref1,
title="Fire detection based on vision sensor and support vector machines",
journal="Fire safety journal",
year="2009",
author="Ko, Byoung Chul and Cheong, Kwang-Ho and Nam, Jae-Yeal",
volume="44",
number="3",
pages="322-329",
abstract="This paper proposes a new vision sensor-based fire-detection method for an early-warning fire-monitoring system. First, candidate fire regions are detected using modified versions of previous related methods, such as the detection of moving regions and fire-colored pixels. Next, since fire regions generally have a higher luminance contrast than neighboring regions, a luminance map is made and used to remove non-fire pixels. Thereafter, a temporal fire model with wavelet coefficients is created and applied to a two-class support vector machines (SVM) classifier with a radial basis function (RBF) kernel. The SVM classifier is then used for the final fire-pixel verification. Experimental results showed that the proposed approach was more robust to noise, such as smoke, and subtle differences between consecutive frames when compared with the other method. Keywords: Fire detection; Vision sensor; Wavelet transform; Support vector machine; Luminance map<p />",
language="",
issn="0379-7112",
doi="10.1016/j.firesaf.2008.07.006",
url="http://dx.doi.org/10.1016/j.firesaf.2008.07.006"
}