Fire-Smoke Detection Based on Video using Dynamic Bayesian Networks 


Vol. 34,  No. 4, pp. 388-396, Apr.  2009


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  Abstract

This paper proposes a new fire-smoke detection method by using extracted features from camera images and pattern recognition technique. First, moving regions are detected by analyzing the frame difference between two consecutive images and generate candidate smoke regions by applying smoke color model. A smoke region generally has a few characteristics such as similar color, simple texture and upward motion. From these characteristics, we extract brightness, wavelet high frequency and motion vector as features. Also probability density functions of three features are generated using training data. Probabilistic models of smoke region are then applied to observation nodes of our proposed Dynamic Bayesian Networks (DBN) for considering time continuity. The proposed algorithm was successfully applied to various fire-smoke tasks not only forest smokes but also real-world smokes and showed better detection performance than previous method.

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  Cite this article

[IEEE Style]

I. Lee, B. Ko, J. Nam, "Fire-Smoke Detection Based on Video using Dynamic Bayesian Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 4, pp. 388-396, 2009. DOI: .

[ACM Style]

In-gyu Lee, ByungChul Ko, and Jae-yeol Nam. 2009. Fire-Smoke Detection Based on Video using Dynamic Bayesian Networks. The Journal of Korean Institute of Communications and Information Sciences, 34, 4, (2009), 388-396. DOI: .

[KICS Style]

In-gyu Lee, ByungChul Ko, Jae-yeol Nam, "Fire-Smoke Detection Based on Video using Dynamic Bayesian Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 4, pp. 388-396, 4. 2009.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880