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Proceedings Paper

Automatic fire detection system using CCD camera and Bayesian network
Author(s): Kwang-Ho Cheong; Byoung-Chul Ko; Jae-Yeal Nam
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Paper Abstract

This paper proposes a new vision-based fire detection method for real-life application. Most previous vision-based methods using color information and temporal variations of pixels produce frequent false alarms due to the use of many heuristic features. Plus, there is usually a computation delay for accurate fire detection. Thus, to overcome these problems, candidate fire regions are first detected using a background model and color model of fire. Probabilistic models of fire are then generated based on the fact that fire pixel values in consecutive frames change constantly and these models are applied to a Bayesian Network. This paper uses a three-level Bayesian Network that contains intermediate nodes, and uses four probability density functions for evidence at each node. The probability density functions for each node are modeled using the skewness of the color red and three high frequency components obtained from a wavelet transform. The proposed system was successfully applied to various fire-detection tasks in real-world environments and effectively distinguished fire from fire-colored moving objects.

Paper Details

Date Published: 26 February 2008
PDF: 12 pages
Proc. SPIE 6813, Image Processing: Machine Vision Applications, 68130S (26 February 2008); doi: 10.1117/12.764822
Show Author Affiliations
Kwang-Ho Cheong, Keimyung Univ. (South Korea)
Byoung-Chul Ko, Keimyung Univ. (South Korea)
Jae-Yeal Nam, Keimyung Univ. (South Korea)

Published in SPIE Proceedings Vol. 6813:
Image Processing: Machine Vision Applications
Kurt S. Niel; David Fofi, Editor(s)

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