[go: up one dir, main page]
Skip to main content
SpringerLink
Log in

Fire Detection in Video Using LMS Based Active Learning

  • Published:
Fire Technology Aims and scope Submit manuscript

Abstract

In this paper, a video based algorithm for fire and flame detection is developed. In addition to ordinary motion and color clues, flame flicker is distinguished from motion of flame colored moving objects using Markov models. Irregular nature of flame boundaries is detected by performing temporal wavelet analysis using Hidden Markov Models as well. Color variations in fire is detected by computing the spatial wavelet transform of moving fire-colored regions. Boundary of flames are represented in wavelet domain and irregular nature of the boundaries of fire regions is also used as an indication of the flame flicker. Decisions from sub-algorithms are linearly combined using an adaptive active fusion method. The main detection algorithm is composed of four sub-algorithms (i) detection of fire colored moving objects, (ii) temporal, and (iii) spatial wavelet analysis for flicker detection and (iv) contour analysis of fire colored region boundaries. Each algorithm yields a continuous decision value as a real number in the range [−1, 1] at every image frame of a video sequence. Decision values from sub-algorithms are fused using an adaptive algorithm in which weights are updated using the least mean square (LMS) method in the training (learning) stage.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Fig. 4
Figure 5
Figure 6
Fig. 7
Fig. 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17

Similar content being viewed by others

References

  1. Phillips W, Shah M, Lobo NV (2002) Flame recognition in video. Pattern Recognit Lett 23:319–327

    Google Scholar 

  2. Mallat S, Zhong S (1992) Characterization of signals from multiscale edges. IEEE Trans Pattern Anal Mach Intell 14(7):710–732

    Google Scholar 

  3. Cetin AE, Ansari R (1994) Signal recovery from wavelet transform maxima. IEEE Trans Sig Process 42:194–196

    Google Scholar 

  4. Quatieri TF (2001) Discrete-time speech signal processing: principles and practice. Prentice-Hall, Indiana

  5. Cetin AE, Jabloun F, Erzin E (1999) Teager energy based feature parameters for speech recognition in car noise. IEEE Sig Pocess Lett 6(10):259–261

    Google Scholar 

  6. Healey G, Slater D, Lin T, Drda B, and Goedeke AD (1993) A system for real-time fire detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 15–17

  7. Celik T, Demirel H, Ozkaramanli H, Uyguroglu M (2007) Fire detection using statistical color model in video sequences. J Vis Commun Image Represent 18(2):176–185

    Google Scholar 

  8. Töreyin BU, Çetin AE (2007) Online detection of fire in video. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1–5

  9. Töreyin BU, Dedeoglu Y, Gudukbay U, and Cetin AE (2006) Computer vision based system for real-time fire and flame detection. Pattern Recognit Lett 27:49–58

    Google Scholar 

  10. Dedeoglu Y, Töreyin BU, Gudukbay U, Cetin AE (2005) Real-time fire and flame detection in video. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 669–672

  11. Töreyin BU, Dedeoglu Y, Cetin AE (2005) Flame detection in video using hidden markov models. In: Proceedings of the IEEE international conference on image processing (ICIP), pp 1230–1233

  12. Widrow B, Hoff ME (1960) Adaptive switching circuits. In: Proceedings of the IRE WESCON (New York Convention Record), vol 4, pp 96–104

  13. Collins RT, Lipton AJ, Kanade T (1999) A system for video surveillance and monitoring. In: Proceedings of the 8-th international topical meeting on robotics and remote systems. April 1999, American Nuclear Society

  14. Bagci M, Yardimci Y, Cetin AE (2002) Moving object detection using adaptive subband decomposition and fractional lower order statistics in video sequences. Signal processing, pp 1941–1947

  15. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), vol 2

  16. Heijden F (1996) Image based measurement systems: object recognition and parameter estimation. Wiley, New York

  17. Bunke H, Caelli T (Eds) (2001) HMMs applications in computer vision. World Scientific, Singapore

  18. Gerek ÖN, Cetin AE (2000) Adaptive polyphase subband decomposition structures for image compression. IEEE Trans Image Process 9:1649–1659

    Article  Google Scholar 

  19. Haykin S (2002) Adaptive filter theory. Prentice Hall, London

  20. Widrow B, Stearns SD (1985) Adaptive signal processing. Prentice Hall, NJ

  21. Schnaufer BA, Jenkins WK (1993) New data-reusing LMS algorithms for improved convergence. In: Proceedings of the Asilomar conference, Pacific Groves, CA pp 1584–1588

  22. Widrow B, McCool JM, Larimore MG, Johnson CR (1976) Stationary and nonstationary learning characteristics of the LMS adaptive filter. Proc IEEE 64(8):1151–1162

    Article  MathSciNet  Google Scholar 

  23. Littlestone N, Warmuth MK (1994) The weighted majority algorithm. Inf Comput 108:212261

    Article  MathSciNet  Google Scholar 

  24. Oza NC (2001) Online ensemble learning. Ph.D. thesis, Electrical Engineering and Computer Sciences, University of California September

Download references

Acknowledgment

This work was supported in part by the Scientific and Technical Research Council of Turkey, TUBITAK, with grant no. 106G126 and 105E191, and in part by European Commission 6th Framework Program with grant number FP6-507752 (MUSCLE Network of Excellence Project).

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Bilkent University, 06800, Bilkent, Ankara, Turkey

    Osman Günay, Kasım Taşdemir, B. Uğur Töreyin & A. Enis Çetin

Authors
  1. Osman Günay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kasım Taşdemir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. Uğur Töreyin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Enis Çetin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Osman Günay.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Günay, O., Taşdemir, K., Uğur Töreyin, B. et al. Fire Detection in Video Using LMS Based Active Learning. Fire Technol 46, 551–577 (2010). https://doi.org/10.1007/s10694-009-0106-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10694-009-0106-8

Keywords

Search

Navigation