Fire Detection Using Multi-Channel Information and Gray Level Co-occurrence Matrix Image Features

Jae-Hyun Jun, Min-Jun Kim, Yong-Suk Jang and Sung-Ho Kim
Volume: 13, No: 3, Page: 590 ~ 598, Year: 2017
10.3745/JIPS.02.0062
Keywords: Color Features, Fire Detection, Texture Features
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Abstract
Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We propose an approach to fire detection using an image processing technique. In this paper, we propose a fire detection method using multi- channel information and gray level co-occurrence matrix (GLCM) image features. Multi-channels consist of RGB, YCbCr, and HSV color spaces. The flame color and smoke texture information are used to detect the flames and smoke, respectively. The experimental results show that the proposed method performs better than the previous method in terms of accuracy of fire detection

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Cite this article
IEEE Style
Jae-Hyun Jun, Min-Jun Kim, Yong-Suk Jang, and Sung-Ho Kim, "Fire Detection Using Multi-Channel Information and Gray Level Co-occurrence Matrix Image Features," Journal of Information Processing Systems, vol. 13, no. 3, pp. 590~598, 2017. DOI: 10.3745/JIPS.02.0062.

ACM Style
Jae-Hyun Jun, Min-Jun Kim, Yong-Suk Jang, and Sung-Ho Kim, "Fire Detection Using Multi-Channel Information and Gray Level Co-occurrence Matrix Image Features," Journal of Information Processing Systems, 13, 3, (2017), 590~598. DOI: 10.3745/JIPS.02.0062.