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Fire outbreak has become a common accident that occurs in several places such as in forests, manufacturing industries, living house and in widely crowded areas. These incidents cause severe damage to nature as well as to living creatures in the affected surroundings. Due to this, the need for efficient fire detection system has been increased rapidly. Using fire detecting sensors has proved to be an efficient solution but its effectiveness on delivering quick results depends on the affinity of fire sources. In the proposed method, we present an economical and affordable fire detection algorithm using video processing techniques which is compatible with CCTV and other stationary surveillance cameras. The algorithm uses an RGB color model with chromatic and dynamic disorder analysis to detect the fire. Fire pixels are detected by the rules of the color model which is mainly dependent on the fire pixel intensity and also the saturation of red color component in the fire pixel. The extracted fire like pixels are authorized by growth combined with the disorder of the fire regions. Furthermore, based on iterative checking the real fire is identified, if it is present then the appropriate signals will be sent. The proposed method is tested on various datasets acquired in real time environments and from the internet. This methodology can be used for fully automatic fire detection surveillance with reduced false true errors.
Fire detected: Original Image vs. Grayscale Image
Rate of Fire pixels detection
This proposed work is a part of the project supported by DST (DST/TWF Division/AFW for EM/C/2017/121) titled A framework for event modeling and detection for Smart Buildings using Vision Systems to Amrita Vishwa Vidyapeetham, Coimbatore.
Srishilesh P S
Sanjay Tharagesh R S
Dr. P. Sridhar
Dr. T. Senthilkumar
Dr. Latha Parameswaran
Srishilesh P.S., Parameswaran L., Sanjay Tharagesh R.S., Thangavel S.K., Sridhar P. (2020) Dynamic and Chromatic Analysis for Fire Detection and Alarm Raising Using Real-Time Video Analysis. In: Smys S., Tavares J., Balas V., Iliyasu A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham
Springer, Cham
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