Early Smoke Detection in Outdoor Space by Spatio-Temporal Clustering Using a Single Video Camera

  • Margarita Favorskaya
  • Konstantin Levtin
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 234)


Video surveillance systems are increasingly being used to monitor urban areas and the landscape. Cameras have been proven near buildings, on bridges, ships, into tunnels. One important application of video surveillance system is the early smoke detection in the outdoor space for alarm generation. A novel video-based method of smoke detection by spatio-temporal clustering involves three developing stages. The first stage connects with any motion detection within a scene. The second stage is based on a color-texture analysis of moving regions to find smoke-like regions. Considering the complex nature of smoke (semi-transparency, spectrum overlapping, randomly motion changes) these two stages are not enough for decision making about early alarm generation. The third stage is enhanced by a spatio-temporal clustering of moving regions with a turbulence parameter connecting with fractal properties of smoke. A spatio-temporal data permit to track effectively a smoke propagation in the outdoor space by using the designed real-time software. Experimental results show that the proposed set of spatial and temporal features well discriminates smoke and non-smoke regions in outdoor scenes with a complex background.


Smoke detection Surveillance Turbulence Video Sequences  


  1. 1.
    Verstockt, S., Merci, B., Lambert, P., van de Walle, R., Sette, B.: State of the art in vision-based and smoke detection. In: Proceedings of the 14th International Conference on Auto-matic Fire Detection, vol. 2, pp. 285–292 (2009)Google Scholar
  2. 2.
    Gunay, O., Tasdemir, K., Toreyin, U., Cetin, A.E.: Video based wildfire detection at night. Fire Saf. J. 44, 860–868 (2009)CrossRefGoogle Scholar
  3. 3.
    Han, D., Lee, B.: Flame and smoke detection method for early real-time detection of a tunnel fire. Fire Saf. J. 44, 951–961 (2009)CrossRefGoogle Scholar
  4. 4.
    Ho, C.-C.: Machine vision-based real-time early flame and smoke detection. Meas. Sci. Technol. 20(4), 450–502 (2009)CrossRefGoogle Scholar
  5. 5.
    Ko, B.C., Cheong, K.-H., Nam, J.-Y.: Fire detection based on vision sensor and support vector machines. Fire Saf. J. 44(3), 322–329 (2009)CrossRefGoogle Scholar
  6. 6.
    Qi, X., Ebert, J.: A computer vision-based method for fire detection in color videos. Int. J. Imaging 2(S09), 22–34 (2009)Google Scholar
  7. 7.
    Celik, T., Demirel, H.: Fire detection in video sequences using a generic color model. Fire Saf. J. 44, 147–158 (2009)CrossRefGoogle Scholar
  8. 8.
    Chen, J., He, Y., Wang, J.: Multi-feature fusion based fast video flame detection. Build. Environ. 45, 1113–1122 (2010)CrossRefGoogle Scholar
  9. 9.
    Habiboglu, Y.H., Gunay, O., Cetin A.E.: Real-time wildfire detection using correlation descriptors. In: 19th European Signal Processing Conference, EUSIPCO 2011, pp. 894–898 (2011)Google Scholar
  10. 10.
    Yamagishi H., Yamaguchi J.: A contour fluctuation data processing method for fire flame detection using a color camera. In: IEEE 26th Annual Conference on IECON of the Industrial Electronics Society, vol. 2, pp. 824–829 (2000)Google Scholar
  11. 11.
    Piccinini, P., Calderara, S., Cucchiara, R.: Reliable smoke detection in the domains of image energy and color. In: Proceedings of the 15th IEEE Conference on Image Processing, pp. 1376–1379 (2008)Google Scholar
  12. 12.
    Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Computer vision based method for real-time fire and flame detection. Pattern Recogn. Lett. 27(1), 49–58 (2006)CrossRefGoogle Scholar
  13. 13.
    Yasmin, R.: Detection of smoke propagation direction using color video sequences. Int. J. Soft Comput. 4(1), 45–48 (2009)Google Scholar
  14. 14.
    Yuan, F.N., Liao, G.X., Fan, W.C., Zhou, H.Q.: Vision based fire detection using mixture Gaussian model. In: Proceedings of the 8th International Symposium on Fire Safety Science, vol. 8, pp. 1575–1583 (2005)Google Scholar
  15. 15.
    Toreyin, B.U., Dedeoglu, A.Y., Cetin, E.: Wavelet based real-time smoke detection in video. In: Proceedings of the 13th European Signal Processing Conference EUSIPCO, pp. 4–8 (2005)Google Scholar
  16. 16.
    Gubbi, J., Marusic, S., Palaniswami, M.: Smoke detection in video using wavelets and support vector machines. Fire Saf. J. 44(8), 1110–1115 (2009)CrossRefGoogle Scholar
  17. 17.
    Ferrari, R.J., Zhang, H., Kube, C.R.: Real-time detection of steam in video images. Pattern Recogn. 40(3), 1148–1159 (2007)CrossRefMATHGoogle Scholar
  18. 18.
    Yuan, F.: A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recogn. Lett. 29(7), 925–932 (2008)CrossRefGoogle Scholar
  19. 19.
    Ojala, T., Pietikainen, M., Maenpaa, T.T.: Multiresolution gray-scale and rotation invariant texture classification with local binary pattern. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987 (2002)Google Scholar
  20. 20.
    Guo, Z.H., Zhang, L., Zhang, D.: Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recogn. 43(3), 706–719 (2009)CrossRefGoogle Scholar
  21. 21.
    Liao, S., Law, M.W.K., Chung, C.S.: Dominant local binary patterns for texture classification. IEEE Trans. Image Process. 18(5), 1107–1118 (2009)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Yuan, F.: Video-based smoke detection with histogram sequence of LBP and LBPV pyra-mids. Fire Saf. J. 46, 132–139 (2011)CrossRefGoogle Scholar
  23. 23.
    Celik, T., Demirel, H., Ozkaramanli, H., Uyguroglu, M.: Fire detection using statistical color model in video sequences. J. Vis. Commun. Image Represent. 18(2), 176–185 (2007)CrossRefGoogle Scholar
  24. 24.
    Yu, C., Zhang, Y., Fang, J., Wang, J.: Video smoke recognition based on optical flow. In: Proceedings of the 2th International Conference on Advanced Computer Control, vol. 2, pp. 16–21 (2010)Google Scholar
  25. 25.
    Favorskaya, M.: Motion estimation for object analysis and detection in videos. In: Kountchev, R., Nakamatsu, K. (eds.) Advances in reasoning-based image processing, analysis and intelligent systems: Conventional and intelligent paradigms, pp. 211–253. Springer-Verlag, Berlin Heidelberg (2012)CrossRefGoogle Scholar
  26. 26.
    Catrakis, H.J., Dimotakis, P.E.: Shape Complexity in Turbulence. Phys. Rev. Lett. 80(5), 968–971 (1998)Google Scholar
  27. 27.
    Maruta, H., Nakamura, A., Kurokawa, F.: A novel smoke detection method using support vector machine. In: IEEE TENCON, pp. 210–215 (2010)Google Scholar
  28. 28.
    Maruta, H., Nakamura, A., Yamamichi, T., Kurokawa, F.: Image based smoke detection with local Hurst exponent. In: IEEE TENCON, pp. 4653–4656 (2010)Google Scholar
  29. 29.
    Wang, S.J., Jeng, D.L., Tsai, M.T.: Early fire detection method in video for vessels. J. Syst. Softw. 82(4), 656–667 (2009)CrossRefGoogle Scholar
  30. 30.
    Vidal-Calleja, T.A., Agammenoni, G.: Integrated probabilistic generative model for detecting smoke on visual images. In: IEEE International Conference on Robotics and Automation River Centre, pp. 2183–2188. ACFR, Saint Paul, Minnesota (2012)Google Scholar
  31. 31.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 8(6), (1986) 679–698Google Scholar
  32. 32.
    Catrakis, H.J., Aguirre, R.C., Ruiz-Plancarte, J., Thayne, R.D.: Shape complexity of whole-field three-dimensional space-time fluid interfaces in turbulence. Phys. Fluids 14(11), 3891–3898 (2002)Google Scholar
  33. 33.
    Favorskaya, M.N., Petukhov, N.Y.: Recognition of natural objects on air photographs using neural networks. J. Opt. Instrum. Data Process. 47(3), 233–238 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Siberian State Aerospace UniversityKrasnoyarskRussian Federation
  2. 2.Siberian State Aerospace University KonstantinRussia

Personalised recommendations