Abstract
This paper presents a combined approach for rapid smoke detection from video sequences using pre-improvement methods. Smoke is characterized by different properties like a color, irregularities in motion, smoothing the edges, etc. There are hard to describe it using basic image features. Usually smoke detection algorithms use some color and turbulence features for the smoke description. But our experiments shown the high false alarm rate of such algorithms. In our work we propose the additional smoke description features based on smoke transparency. Before the recognition we use the regions matching method to increase the flexibility of our system. As a first step in processing we extract background. Moving objects are candidates for smoke. The Gray World algorithm is used here. Compare the results with the original frames in order to get image features within some particular gray scale interval. After that we use the rate of color changing to checking of the transparency of current area. Last we calculate complexity of turbulent phenomena of the smoke shape and apply it to the incoming video stream. As a result we have just smoke regions on the video stream. There are different objects, shadows or illumination changes will not be mistaken for smoke by the algorithm. This method gives an early recognition of smoke in the observed scene.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Buchsbaum, G.: A spatial processor model for object color perception. J. Franklin Inst. 310(1), 1–26 (1980)
Catrakis, H.J., Dimotakis, P.E.: Shape Complexity in Turbulence. J. Phys. Rev. Lett. 80(5), 968–971 (1998)
Celik, T., Ozkaramanly, H., Demire, H.: Fire and Smoke Detection Without Sensors: Image Processing Approach. In: Proceedings of 15th European Signal Processing Conference EUSIPCO, pp. 1794–1798 (2007)
Chunyu, Y., Jun, F., Jinjun, W., Yongming, Z.: Video Fire Smoke Detection Using Motion and Color Features. J. Fire Technology 46(3), 651–663 (2010)
Fujiwara, N., Terada, K.: Extraction of a smoke region using fractal coding. In: IEEE International Symposium on Communications and Information Technology, ISCIT 2004, October 26-29, vol. 2, pp. 659–662 (2004)
Grech-Cini, H.J.: Smoke Detection. US Patent No. US6844818B2
Gudukbayb, U., Enis Cetin, A.: Computer vision based method for real-time fire and flame detection. Pattern Recognition Letters 27(1), 49–58 (2006)
Kopilovic, I., Vagvolgyi, B., Sziranyi, T.: Application of panoramic annular lens for motion analysis tasks: surveillance and smoke detection. In: Proceedings of 15th International Conference on Pattern Recognition, September 3-7, vol. 4, pp. 714–717 (2000)
Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Contour Based Smoke Detection in Video Using Wavelets. In: Proceedings European Signal Processing Conference, EUSIPCO 2006 (2006)
Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Wavelet based real-time smoke detection in video. In: Procedings of EUSIPCO 2005 (2005)
Vicente, J., Guillemant, P.: An image processing technique for automatically detecting forest fire. International Journal of Thermal Sciences 41(12), 1113–1120 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Anton, M., Olga, K. (2013). Real-Time Smoke Detection in Video Sequences: Combined Approach. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2013. Lecture Notes in Computer Science, vol 8251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45062-4_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-45062-4_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45061-7
Online ISBN: 978-3-642-45062-4
eBook Packages: Computer ScienceComputer Science (R0)