Single Image Smoke Detection

  • Hongda TianEmail author
  • Wanqing Li
  • Philip Ogunbona
  • Lei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)


Despite the recent advances in smoke detection from video, detection of smoke from single images is still a challenging problem with both practical and theoretical implications. However, there is hardly any reported research on this topic in the literature. This paper addresses this problem by proposing a novel feature to detect smoke in a single image. An image formation model that expresses an image as a linear combination of smoke and non-smoke (background) components is derived based on the atmospheric scattering models. The separation of the smoke and non-smoke components is formulated as convex optimization that solves a sparse representation problem. Using the separated quasi-smoke and quasi-background components, the feature is constructed as a concatenation of the respective sparse coefficients. Extensive experiments were conducted and the results have shown that the proposed feature significantly outperforms the existing features for smoke detection.


Support Vector Machine Sparse Representation Local Binary Pattern Block Image Light Smoke 
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.



This work was partly supported by SNS Unicorp Pty Ltd.


  1. 1.
    Calderara, S., Piccinini, P., Cucchiara, R.: Vision based smoke detection system using image energy and color information. Mach. Vis. Appl. 22, 705–719 (2011)CrossRefGoogle Scholar
  2. 2.
    Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Wavelet based real-time smoke detection in video. In: EUSIPCO (2005)Google Scholar
  3. 3.
    Yuan, F.: A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with adaboost for video smoke detection. Pattern Recogn. 45, 4326–4336 (2012)CrossRefGoogle Scholar
  4. 4.
    Tian, H., Li, W., Wang, L., Ogunbona, P.: A novel video-based smoke detection method using image separation. In: ICME, pp. 532–537 (2012)Google Scholar
  5. 5.
    Tian, H., Li, W., Wang, L., Ogunbona, P.: Smoke detection in video: an image separation approach. IJCV 106, 192–209 (2014)CrossRefGoogle Scholar
  6. 6.
    Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. IJCV 48, 233–254 (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    Yuan, F.: A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recogn. Lett. 29, 925–932 (2008)CrossRefGoogle Scholar
  8. 8.
    Kolesov, I., Karasev, P., Tannenbaum, A., Haber, E.: Fire and smoke detection in video with optimal mass transport based optical flow and neural networks. In: ICIP (2010)Google Scholar
  9. 9.
    Park, J., Ko, B., Nam, J.Y., Kwak, S.: Wildfire smoke detection using spatiotemporal bag-of-features of smoke. In: WACV, pp. 200–205 (2013)Google Scholar
  10. 10.
    Jakovcevic, T., Stipanicev, D., Krstinic, D.: Visual spatial-context based wildfire smoke sensor. Mach. Vis. Appl. 24, 707–719 (2013)CrossRefGoogle Scholar
  11. 11.
    Labati, R.D., Genovese, A., Piuri, V., Scotti, F.: Wildfire smoke detection using computational intelligence techniques enhanced with synthetic smoke plume generation. IEEE Trans. Syst. Man Cybern.: Syst. 43, 1003–1012 (2013)CrossRefGoogle Scholar
  12. 12.
    Morerio, P., Marcenaro, L., Regazzoni, C.S., Gera, G.: Early fire and smoke detection based on colour features and motion analysis. In: ICIP, pp. 1041–1044 (2012)Google Scholar
  13. 13.
    Wang, Y., Chua, T.W., Chang, R., Pham, N.T.: Real-time smoke detection using texture and color features. In: ICPR, pp. 1727–1730 (2012)Google Scholar
  14. 14.
    Tian, H., Li, W., Ogunbona, P., Nguyen, D.T., Zhan, C.: Smoke detection in videos using non-redundant local binary pattern-based features. In: MMSP, pp. 1–4 (2011)Google Scholar
  15. 15.
    Long, C., et al.: Transmission: a new feature for computer vision based smoke detection. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds.) Artificial Intelligence and Computational Intelligence. LNCS, vol. 6319, pp. 389–396. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Maruta, H., Nakamura, A., Yamamichi, T., Kurokawa, F.: Image based smoke detection with local hurst exponent. In: ICIP, pp. 4653–4656 (2010)Google Scholar
  17. 17.
    Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T.S., Yan, S.: Sparse representation for computer vision and pattern recognition. Proc. IEEE 98, 1031–1044 (2010)CrossRefGoogle Scholar
  18. 18.
    Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: NIPS (2007)Google Scholar
  19. 19.
    Bezdek, J.C., Hathaway, R.J.: Convergence of alternating optimization. Neural Parallel Sci. Computations 11, 351–368 (2003)zbMATHMathSciNetGoogle Scholar
  20. 20.
    Bai, X., Sapiro, G.: Geodesic matting: a framework for fast interactive image and video segmentation and matting. IJCV 82, 113–132 (2009)CrossRefGoogle Scholar
  21. 21.
    Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE TPAMI 30, 228–242 (2008)CrossRefGoogle Scholar
  22. 22.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE TPAMI 33, 2341–2353 (2011)CrossRefGoogle Scholar
  23. 23.
    Starck, J.L., Elad, M., Donoho, D.L.: Image decomposition via the combination of sparse representations and a variational approach. IEEE TIP 14, 1570–1582 (2005)zbMATHMathSciNetGoogle Scholar
  24. 24.
    Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54, 4311–4322 (2006)CrossRefGoogle Scholar
  25. 25.
    Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical report (2009)Google Scholar
  26. 26.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006)Google Scholar
  27. 27.
    Fattal, R.: Single image dehazing. ACM Trans. Graph. 27, 1–9 (2008)CrossRefGoogle Scholar
  28. 28.
    Kopf, J., Neubert, B., Chen, B., Cohen, M., Cohen-Or, D., Deussen, O., Uyttendaele, M., Lischinski, D.: Deep photo: model-based photograph enhancement and viewing. ACM Trans. Graph. 27 (2008)Google Scholar
  29. 29.
    Tan, R.T.: Visibility in bad weather from a single image. In: CVPR (2008)Google Scholar
  30. 30.
    Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22, 747–757 (2000)CrossRefGoogle Scholar
  31. 31.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE TPAMI 24, 971–987 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hongda Tian
    • 1
    Email author
  • Wanqing Li
    • 1
  • Philip Ogunbona
    • 1
  • Lei Wang
    • 1
  1. 1.Advanced Multimedia Research Lab, ICT Research Institute, School of Computer Science and Software EngineeringUniversity of WollongongWollongongAustralia

Personalised recommendations