Fast and Efficient Method for Fire Detection Using Image Processing

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may t...

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Bibliographic Details
Published in:ETRI journal Vol. 32; no. 6; pp. 881 - 890
Main Author: Celik, Turgay
Format: Journal Article
Language:English
Published: 한국전자통신연구원 01.12.2010
Subjects:
color modeling
Fire detection
image processing
image segmentation
motion detection
video processing
전자/정보통신공학
ISSN:1225-6463, 2233-7326
Online Access:Get full text
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Summary:Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision‐based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand‐alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE L*a*b* color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state‐of‐the‐art fire detection method.
Bibliography:G704-001110.2010.32.6.008
ISSN:1225-6463
2233-7326
DOI:10.4218/etrij.10.0109.0695