Fire detection with video using fuzzy c-means and back-propagation neural network

Authors:
Tung Xuan Truong;Jong-Myon Kim
Affiliations:
School of Computer Engineering and Information Technology, University of Ulsan, Korea;School of Computer Engineering and Information Technology, University of Ulsan, Korea
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Year:
2011

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Abstract

In this paper, we propose an effective method that detects fire automatically. The proposed algorithm is composed of four stages. In the first stage, an approximate median method is used to detect moving regions. In the second stage, a fuzzy c-means (FCM) algorithm based on the color of fire is used to select candidate fire regions from these moving regions. In the third stage, a discrete wavelet transform (DWT) is used to derive the approximated and detailed wavelet coefficients of sub-image. In the fourth stage, using these wavelet coefficients, a back-propagation neural network (BPNN) is utilized to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and low false alarm rate.