Skip to main content
SpringerLink
Log in

Single Image Smoke Detection

  • Conference paper
  • First Online:
Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9004))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Article  Google Scholar 

  2. Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Wavelet based real-time smoke detection in video. In: EUSIPCO (2005)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Tian, H., Li, W., Wang, L., Ogunbona, P.: Smoke detection in video: an image separation approach. IJCV 106, 192–209 (2014)

    Article  Google Scholar 

  6. Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. IJCV 48, 233–254 (2002)

    Article  MATH  Google Scholar 

  7. Yuan, F.: A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recogn. Lett. 29, 925–932 (2008)

    Article  Google Scholar 

  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. 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. Jakovcevic, T., Stipanicev, D., Krstinic, D.: Visual spatial-context based wildfire smoke sensor. Mach. Vis. Appl. 24, 707–719 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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)

    Chapter  Google Scholar 

  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. 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)

    Article  Google Scholar 

  18. Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: NIPS (2007)

    Google Scholar 

  19. Bezdek, J.C., Hathaway, R.J.: Convergence of alternating optimization. Neural Parallel Sci. Computations 11, 351–368 (2003)

    MATH  MathSciNet  Google Scholar 

  20. Bai, X., Sapiro, G.: Geodesic matting: a framework for fast interactive image and video segmentation and matting. IJCV 82, 113–132 (2009)

    Article  Google Scholar 

  21. Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE TPAMI 30, 228–242 (2008)

    Article  Google Scholar 

  22. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE TPAMI 33, 2341–2353 (2011)

    Article  Google Scholar 

  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)

    MATH  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  25. Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical report (2009)

    Google Scholar 

  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. Fattal, R.: Single image dehazing. ACM Trans. Graph. 27, 1–9 (2008)

    Article  Google Scholar 

  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. Tan, R.T.: Visibility in bad weather from a single image. In: CVPR (2008)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Acknowledgement

This work was partly supported by SNS Unicorp Pty Ltd.

Author information

Authors and Affiliations

  1. Advanced Multimedia Research Lab, ICT Research Institute, School of Computer Science and Software Engineering, University of Wollongong, Wollongong, Australia

    Hongda Tian, Wanqing Li, Philip Ogunbona & Lei Wang

Authors
  1. Hongda Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wanqing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Philip Ogunbona
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongda Tian .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Tian, H., Li, W., Ogunbona, P., Wang, L. (2015). Single Image Smoke Detection. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16808-1_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16807-4

  • Online ISBN: 978-3-319-16808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation