Citation

Khalil A, Rahman SU, Alam F, Ahmad I, Khalil I. Fire Technol. 2021; 57(3): 1221-1239.

Copyright

(Copyright © 2021, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s10694-020-01030-9

PMID

unavailable

Abstract

Emergency incidents and events of fires can be dangerous and required quick and accurate decision-making need quick and correct decision-making. The use of computer vision for fire detection can provide a efficient solution to deal with these situations. These systems handle the usual data, provide an automated solution, and discard non-relevant information without discarding relevant content. Researchers developed many techniques for fire detection in videos and still images by using different color-based models. However, for videos, these methods are unsuitable because of high false-positive results. These methods use few parameters with little physical meaning, which makes fire detection more difficult. To deal with this, we have proposed a novel fire detection method based on Red Green Blue and CIE $$L * a * b$$color models, by combining motion detection with tracking fire objects. We have eliminated the moving region and calculate the growth rate of the fire to reduce false-alarm and calculate the risk. The proposed method operates on a reduced number of parameters compared to the existing methods. Experimental results demonstrate the effectiveness of our method of reducing false positives while keeping their precision compatible with the existing methods.

Language: en

Keywords

Fire detection; Fire growth; Static object tracking