DOI QR코드

DOI QR Code

Reliable Smoke Detection using Static and Dynamic Textures of Smoke Images

연기 영상의 정적 및 동적 텍스처를 이용한 강인한 연기 검출

  • 김재민 (홍익대학교 전자전기공학부)
  • Received : 2011.12.20
  • Accepted : 2012.02.02
  • Published : 2012.02.28

Abstract

Automatic smoke detection systems using a surveillance camera requires a reliable smoke detection method. When an image sequence is captured from smoke spreading over in the air, not only has each smoke image frame a special texture, called static texture, but the difference between two smoke image frames also has a peculiar texture, called dynamic texture. Even though an object has a static texture similar to that of the smoke, its dynamic texture cannot be similar to that of the smoke if its movement differs from the diffraction action of the smoke. This paper presents a reliable smoke detection method using these two textures. The proposed method first detects change regions using accumulated frame difference, and then picks out smoke regions using Haralick features extracted from two textures.

Keywords

Smoke Detection;Temporal Features;Spatial Features;Texture;Haralick Features

Acknowledgement

Supported by : 홍익대학교

References

  1. N. Fujiwara and K. Terada, "Extraction of a smoke region using fractal coding," IEEE International symposium on communication and information technology, Vol.2, pp.659-662, 2004(10).
  2. I. Kopilovic, b. Vagvolgyi, and T. Sziranyi, "Application of panoramic annular lens for motion analysis tasks: surveillance and smoke detection," Proceedings of 15th international conference on pattern recognition, Vol.4, pp.714-717, 2000(9).
  3. J. Vicente and P. Guillemant, "An image processing technique for automatically detecting forest fire," International Journal of Thermal Sciences, Vol.41, No.12, pp.1113-1120, 2002. https://doi.org/10.1016/S1290-0729(02)01397-2
  4. T. T. Truong and J. M. Kim "Early smoke detection system based on motion estimation," IFOST 2010 Proceedings, pp.437-440, 2010(10).
  5. B. U. Toreyin, Y. Dedeoglu, and A. E. Cetin, "Wavelet based real-time smoke detection in video," 13th European Signal Processing Conference EUSIPCO, 2005.
  6. S. Calderara, P. Piccinini, and R. Cucchiara, "Smoke detection in video surveillance: A MoG model in the wavelet domain," ICVS 2008, LNCS 5008, pp.119-128, 2008.
  7. A. Rafiee, and R. Tavakoli, "Fire and Smoke Detection using Wavelet Analysis and Disorder Characteristics," ICCRD, pp.262-265, 2011(3).
  8. H. J. Grech-Cini, "Smoke detection," US Patent No. US6844818B2, 2005(1).
  9. Z. Xiong, R. Caballero, H. Wang, A. M. Finn, M. A. Lelic, and P. Y. Peng, "Video-based smoke detection: possibilities, techniques, and challenges," SUPDET, Orlando, FL 2007.
  10. Shen-Kuen, "Smoke detecting method and device," US Patent No. US7859419B2, 2008(12).
  11. H. Maruta, A. Nakamura, and F. Kurokawa, "A New Approach for Smoke Detection with Texture Analysis and Support Vector Machine," IEEE International Symposium on Industrial Electronics, pp.1550-1555, 2010(7).
  12. R. M. Haralic and K. Shanmugam, "Textural Features for Image Classification," IEEE Transactions on Systems, Man, and Cybernetics Vol.3, No.6, pp.610-621, 1973.
  13. http://www.csie.ntu.edu.tw/-cjlin/libsvm
  14. P. L. Rosin and E. Ioannidis, "Evaluation of global image thresholding for change detection," Pattern Recognition Letters, Vol.24, pp.2345-2356, 2003. https://doi.org/10.1016/S0167-8655(03)00060-6
  15. C. Su and A. Amer, "A real-time adaptive thresholding for video change detection," IEEE International Conference on Image Processing, pp.157-160, 2006(10).
  16. J. M. McHugh and J. Konrad, "Foreground Adaptive Background Subtraction," IEEE Signal Process. Lett, Vol.16, No.5, pp.390-393, 2009. https://doi.org/10.1109/LSP.2009.2016447