Publication Type : Book Chapter
Publisher : Lecture Notes in Computational Vision and Biomechanics
Source : Lecture Notes in Computational Vision and Biomechanics, Springer Netherlands, Volume 30, p.1029-1038 (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060191234&doi=10.1007%2f978-3-030-00665-5_99&partnerID=40&md5=f9d054b1ebfff60441f60ff6b10eff7b
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2019
Abstract :
Recent technological advancement has opened the space for a gradual increase in the number of smart buildings. Public safety and security has becomes a matter of concern with such a development, especially in areas of fire accidents. The conventional fire detection system usually worked on sensors and takes time for fire detection. This work presents an early fire detection system that unlike conventional fire detection system is cost-effective with high fire detection rate. The proposed algorithm uses features like color, increase in area and intensity flicker for early detection of fire. Segmentation of fire colored regions is done with the help of L*a*b*, YCbCr, and RGB color space. Analysis of fire, i.e., fire area, its spread, temporal information, direction of the fire, and its average growth rate are measured using optical flow and blob analysis. Accuracy and F measure are used to evaluate the accuracy of the proposed system. Experimental results show that the average accuracy of the system is above 80% which is more promising in a video. © Springer Nature Switzerland AG 2019.
