Skip to main content
SpringerLink
Log in

An effective algorithm to detect both smoke and flame using color and wavelet analysis

  • Applied Problems
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

Fire detection is an important task in many applications. Smoke and flame are two essential symbols of fire in images. In this paper, we propose an algorithm to detect smoke and flame simultaneously for color dynamic video sequences obtained from a stationary camera in open space. Motion is a common feature of smoke and flame and usually has been used at the beginning for extraction from a current frame of candidate areas. The adaptive background subtraction has been utilized at a stage of moving detection. In addition, the optical flow-based movement estimation has been applied to identify a chaotic motion. With the spatial and temporal wavelet analysis, Weber contrast analysis and color segmentation, we achieved moving blobs classification. Real video surveillance sequences from publicly available datasets have been used for smoke detection with the utilization of our algorithm. We also have conducted a set of experiments. Experiments results have shown that our algorithm can achieve higher detection rate of 87% for smoke and 92% for flame.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. S. G. Kong, D. Jin, S. Li, and H. Kim, “Fast fire flame detection in surveillance video using logistic regression and temporal smoothing,” Fire Safety J. 79, 37–43 (2016).

    Article  Google Scholar 

  2. P. Piccinini, S. Calderara, and R. Cucchiara, “Reliable smoke detection system in the domains of image energy and color,” in Proc. 15th IEEE Conf. on Image Processing ICIP (San Diego, 2008), pp. 1376–1379.

    Google Scholar 

  3. B. U. Toreyin, et al., “Wavelet based real-time smoke detection in video,” Signal Processing: Image Communication, EURASIP No. 20, 255–256 (2005).

    Google Scholar 

  4. D. Kim and Y. Wang, “Smoke detection in video,” in Proc. WCCSIE (Los Angeles, 2009), pp. 759–763.

    Google Scholar 

  5. B. U. Toreyin, Y. Dedeoglu, and A. Cetin, “Contour based smoke detection in video using wavelets,” in Proc. ESPC (Saguenay, 2006), pp. 123–128.

    Google Scholar 

  6. F. Comez-Rodriuez, et al., “Smoke monitoring and measurement using image processing. Application to forest fires,” in Proc. SPIE Conf. on Automatic Target Recognition (2003), pp. 404–411.

    Google Scholar 

  7. D. Kristinic, T. Jakovcevic, and D. Stipanicev, “Histogram- based smoke segmentation in forest fire detection system,” Inf. Technol. Control. 38 (3), 237–244 (2009).

    Google Scholar 

  8. W. Phillips, M. Shan, and V. Lobo, “Flame recognition in video,” Pattern Recogn. Lett. 23 (1-3), 319–327 (2002).

    Article  MATH  Google Scholar 

  9. B. U. Toreyin, Y. Dedeoglu, U. Gudukbay, and A. E. Cetin, “Computer vision based method for realtime fire and flame detection,” Pattern Recogn. Lett. 27 (1), 49–58 (2006).

    Article  Google Scholar 

  10. B. U. Toreyin, Y. Dedeoglu, and Y. Enis-Cetin, “Flame detection in video using hidden Markov models,” in Proc. IEEE Int. Conf. on Image Processing ICIP (Genova, 2005), Vol. 2, pp. 1230–1233.

    Google Scholar 

  11. Y. Habiboglu, O. Gunay, and A. E. Cetin, “Flame detection method in video using covariance descriptors,” in Proc. ICASSP (Prague, 2011), pp. 1817–1820.

    Google Scholar 

  12. C. Yu, Z. Mei, and X. Zhang, “A real-time video fire flame and smoke detection algorithm,” in Proc. 9th Asia-Oceania Symp. on Fire Science and Technology (Hefei, 2013), Vol. 62, pp. 891–898.

    Google Scholar 

  13. A. Filonenko, D. C. Hernandez, A. Shahbaz, and K.-H. Jo, “Unified smoke and flame detection for intelligent surveillance system,” in Proc. ISIE 2016 (Santa Clara, 2016).

    Google Scholar 

  14. R. T. Collins, “A system for video surveillance and monitoring,” in Proc. American Nuclear Society 8th Int. Topical Meeting on Robotics and Remote Systems (Pittsburgh, 1999), pp. 68–73.

    Google Scholar 

  15. T. Porter and T. Duff, “Compositing digital images,” Comput. Graph. 18 (3), 253–259 (1984).

    Article  Google Scholar 

  16. G. Farneback, “Two-frame motion estimation based on polynomial expansion,” in Proc. 13th Scandinavian Conf. on Image Analysis (Gothenburg, 2003), pp. 363–370.

    Google Scholar 

  17. T. Celik and H. Demirel, “Fire detection in video sequences using a generic color model,” Fire Safety J. 44 (2), 147–158 (2009).

    Article  Google Scholar 

  18. K. Avgerinakis, A. Briassouli, and I. Kompatsiaris, “Smoke detection using temporal HOGHOF descriptors and energy colour statistics from video,” in Proc. Int. Workshop on Multi-Sensor Systems and Networks for Fire Detection and Management Fire Sense (Antalya, 2012), pp. 3–8.

    Google Scholar 

  19. L. Chen-Yu Lee, Chin-Teng Lin, Chao-Ting Hong, and Miin-Tsair Su, “Smoke detection using spatial and temporal analyses,” Int. J. Innovative Comput., Inf. Control 8 (8), 1–11 (2012).

    Google Scholar 

  20. W. H. Li, B. Fu, L. Xiao, Y. Wang, and P. X. Liu, “A video smoke detection algorithm based on wavelet energy and optical flow Eigen-values,” J. Software 8 (1), 63–69 (2013).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Zhejiang Shuren University, Hangzhou, People’s Republic of China

    Shiping Ye, Zhican Bai & Huafeng Chen

  2. Polotsk State University, Novopolotsk, Republic of Belarus

    R. Bohush

  3. Belarusian State University, Minsk, Republic of Belarus

    S. Ablameyko

Authors
  1. Shiping Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhican Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huafeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R. Bohush
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. Ablameyko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huafeng Chen.

Additional information

The article is published in the original.

Shiping Ye. Born in 1967. Professor and Vice President of Zhejiang Shuren University. Graduated from Zhejiang University in 1988. In 2009 he got his master’s degree in Computer Science and Technology from Zhejiang University. His scientific interests include application of computer graphics and image, GIS. He has published more than 40 academic articles. Four research projects he has taken part in have been awarded second prize of Zhejiang Provincial Scientific and Technological Achievement. Two teaching research programs he has presided over have been awarded first prize and second prize of Zhejiang Provincial Teaching Achievement respectively.

Zhican Bai. Born in 1962. Assistant Professor of Zhejiang Shuren University. Graduated from Hangzhou Normal University in 1984. His scientific interests include digital image processing and computer network technology. He has published 10 academic articles and 4 books.

Huafeng Chen. Born in 1982. Lecturer of Zhejiang Shuren University. Graduated from Zhejiang University in 2003. In 2009 he got his PhD in the field of Earth Exploration and Information Technology at the Institute of Space Information & Technique, Zhejiang University. His scientific interests include remote sensing image processing, GIS application, image and video processing, multi-agent system. He has published 5 academic articles.

Rykhard Bohush. Graduated from Polotsk State University in 1997. In 2002 he got his PhD in the field of Information Processing at the Institute of Engineering Cybernetics, the National Academy of Sciences of Belarus. Head of Computer Systems and Networks Department of Polotsk State University. He is a member of the National Qualifications Framework of Higher Education of Belarus in IT and Electronics Science. His scientific interests include image and video processing, object representation and recognition, intelligent systems, digital steganography. Author of approximately 120 works, including one book on image processing.

Sergey Ablameyko. Born in 1956, DipMath in 1978, PhD in 1984, DSc in 1990, Prof in 1992. Rector (President) of Belarusian State University from 2008. His scientific interests are: image analysis, pattern recognition, digital geometry, knowledge based systems, geographical information systems, medical imaging. He has more than 400 publications. He is in Editorial Board of Pattern Recognition Letters, Pattern Recognition and Image Analysis and many other international and national journals. He is Editor-in-Chief of two national journals. He is a senior member of IEEE, Fellow of IAPR, Fellow of Belarusian Engineering Academy, Academician of National Academy of Sciences of Belarus, Academician of the European Academy, and others. He was a First Vice-President of International Association for Pattern Recognition IAPR (2006-2008), President of Belarusian Association for Image Analysis and Recognition. He is a Deputy Chairman of Belarusian Space Committee, Chairman of BSU Academic Council of awarding of PhD and DSc degrees. For his activity he was awarded by State Prize of Belarus (highest national scientific award) in 2002, Belarusian Medal of F. Skoryna, Russian Award of Friendship and many other awards.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, S., Bai, Z., Chen, H. et al. An effective algorithm to detect both smoke and flame using color and wavelet analysis. Pattern Recognit. Image Anal. 27, 131–138 (2017). https://doi.org/10.1134/S1054661817010138

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1054661817010138

Keywords

Search

Navigation