Abstract
Computer vision - based fire detection has recently attracted a great deal of attention from the research community. In this paper, the authors propose and analyse a new approach for identifying fire in videos. In this approach, we propose a combined algorithm for detecting the fire in videos based on the changes of the statistical features in the fire regions between different frames. The statistical features consist of the average of the red, green and blue channel, the coarseness and the skewness of the red channel distribution. These features are evaluated, and then classified by Bayes classifier, and the final result is defined as fire-alarm rate for each frame. Experimental results demonstrate the effectiveness and robustness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ho, C.-C.: Machine vision-based real-time early flame and smoke detection. Meas. Sci. Technol. 20(4), 045502, 13 p (2009)
Borges, P.V.K., Izquierdo, E.: A probabilistic approach for vision-based fire detection in videos. IEEE Trans. Circuits Syst. Video Technol. 20(5), 721–731 (2010)
Celik, T., Ozkaramanl, H., Demirel, H.: Fire and smoke detection without sensors: image processing - based approach. In: Proc. 15th European Signal Processing Conf., pp. 1794–1798 (2007)
Ko, B.C., Cheong, K., Nam, J.: Fire detection based on vision sensor and support vector machines. Fire Safety J. 44(3), 322–329 (2009)
Duong, H.D., Tinh, D.T.: A Novel Computational Approach for Fire Detection. In: Proc. of KSE 2010 The Second International Conference on Knowledge and Systems Engineering, Hanoi, Vietname, pp. 9–13 (2010)
Habiboglu, Y.H., Günay, O., Çetin, A.E.: Covariance matrix-based fire and flame detection method in video. Machine Vision and Applications, doi: 10.1007/s00138-011-0369-1
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1992)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic, New York (2006)
Fire detection sample video clips, http://signal.ee.bilkent.edu.tr/VisiFire
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Duong, H.D., Tinh, D.T. (2012). A New Approach to Vision-Based Fire Detection Using Statistical Features and Bayes Classifier. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-34859-4_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34858-7
Online ISBN: 978-3-642-34859-4
eBook Packages: Computer ScienceComputer Science (R0)