IEIE Transactions on Smart Processing and Computing

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Fire Detection using Color and Motion Models

  • Lee, Dae-Hyun (INMC, Department of Electrical and Computer Engineering, Seoul National University) ;
  • Lee, Sang Hwa (INMC, Department of Electrical and Computer Engineering, Seoul National University) ;
  • Byun, Taeuk (Department of Convergence Technology, Hoseo Graduate School of Venture) ;
  • Cho, Nam Ik (INMC, Department of Electrical and Computer Engineering, Seoul National University)
  • Received : 2017.04.19
  • Accepted : 2017.05.19
  • Published : 2017.08.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents a fire detection algorithm using color and motion models from video sequences. The proposed method detects change in color and motion of overall regions for detecting fire, and thus, it can be implemented in both fixed and pan/tilt/zoom (PTZ) cameras. The proposed algorithm consists of three parts. The first part exploits color models of flames and smoke. The candidate regions in the video frames are extracted with the hue-saturation-value (HSV) color model. The second part models the motion information of flames and smoke. Optical flow in the fire candidate region is estimated, and the spatial-temporal distribution of optical flow vectors is analyzed. The final part accumulates the probability of fire in successive video frames, which reduces false-positive errors when fire-like color objects appear. Experimental results from 100 fire videos are shown, where various types of smoke and flames appear in indoor and outdoor environments. According to the experiments and the comparison, the proposed fire detection algorithm works well in various situations, and outperforms the conventional algorithms.

Keywords

References

  1. A. Enis Cetin, et al, "Video fire detection - review," Digital Signal Processing, Vol. 23, pp. 1827-1843, 2013. https://doi.org/10.1016/j.dsp.2013.07.003
  2. Thou-ho Chen, Ping-Hsueh Wu, and Yung-Chuen Chiou, "An early fire-detection method based on image processing," Proc. of International Conf. on Image Processing (ICIP), pp.1707-1710, 2004.
  3. Che-Bin Liu and Narendra Ahuja, "Vision based fire detection," Proc. of International Conf. on Pattern Recognition (ICPR), 2004.
  4. B. U. Toreyin, Y. Dedeoglu, and A. Enis Cetin, "Wavelet based real-time smoke detection in video," Proc. of European Signal Proc. Conf. (EUSIPCO), 2005.
  5. B. U. Toreyin, Y. Dedeoglu, U. Gudukbay, and A. Enis Cetin, "Computer vision based method for realtime fire and flame detection," Pattern Recognition Letters, (27), pp. 49-58, 2007.
  6. B. U. Toreyin, R. G. Cinbis, Y. Dedeoglu, and A. Enis Cetin, "Fire detection in infrared video using wavelet analysis," Optical Engineering, 46 (6), pp. 1- 9, 2007.
  7. T. Celik, H. Demirel, H. Ozkaranmanli, and M. Uyguroglu, "Fire detection using statistical color model in video sequences," Journal of Visual Communication and Image Representation, 18, pp.176-185, 2007. https://doi.org/10.1016/j.jvcir.2006.12.003
  8. Feiniu Yuan, "A fast accumulative orientation model based on integral image for video smoke detection," Pattern Recognition Letters, 29, pp.925-932, 2008. https://doi.org/10.1016/j.patrec.2008.01.013
  9. B. C. Ko, K.-H. Cheong, and J-Y. Nam, "Fire detection based on vision sensor and support vector machines," Fire Safety Journal, 44, pp. 322-329, 2009. https://doi.org/10.1016/j.firesaf.2008.07.006
  10. J. Gubbi, S. Marusic, M. Palaniswami, "Smoke detection in video using wavelets and support vector machines," Fire Safety Journal, 44, pp. 1110-1115, 2009. https://doi.org/10.1016/j.firesaf.2009.08.003
  11. Xiao-bai Li, Ying Hua, and Ning Xia, "Fire detection technology based on dynamic texture," Procedia Engineering, 52, pp. 186-195, 2013. https://doi.org/10.1016/j.proeng.2013.02.125
  12. M. Bugaric, T. Jakovevic, and D. Stipanicev, "Adaptive estimation of visual smoke detection parameters based on spatial data and fire risk index," Computer Vision and Image Computing, 118, pp. 184-196, 2014. https://doi.org/10.1016/j.cviu.2013.10.003
  13. D.-C. Wang, X. Cui, E. Park, C. Jin, and H. Kim, "Adaptive flame detection using randomness testing and robust features," Fire Safety Journal, 55, pp. 116-125, 2013. https://doi.org/10.1016/j.firesaf.2012.10.011
  14. T. Xuan and J.-M. Kim, "Fire flame detection in video sequence using multi-stage pattern recognition techniques," Engineering Applications of Artificial Inteligence, 25, pp. 1365-1372, 2012. https://doi.org/10.1016/j.engappai.2012.05.007
  15. Y. Habiboglu, O. Gunay, and A. Cetin, "Covariance matrix-based fire and flame detection method in video," Machine Vision and Applications, Vol. 23, no. 6, pp. 1-11, 2011.
  16. K. V. Mardia and P. E. Jupp, Directional Statistics, Wiley, 1999, pp. 83-92.
  17. G. Farneback, "Two-frame motion estimation based on polynomial expansion," in Proc. Image Anal., pp. 363-370, 2003.
  18. D. T. Nghia, S. Kim, V. L. Cuong, and H.-J. Lee, "Anomalous Trajectory Detection in Surveillance Systems Using Pedestrian and Surrounding Information," IEIE Transactions on Smart Processing and Computing, 5(4), pp. 256-266, Aug. 2016. https://doi.org/10.5573/IEIESPC.2016.5.4.256
  19. H. Kim, J. Park, S. Park and J. Paik, "Motion Estimation-based Human Fall Detection for Visual Surveillance," IEIE Transactions on Smart Processing and Computing, 5(5), pp. 327-330, Oct. 2016. https://doi.org/10.5573/IEIESPC.2016.5.5.327
  20. T. H. Kil, S. H. Lee, and N. I. Cho, "A dehazing algorithm using dark channel prior and contrast enhancement," Proc. of International Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 2484-2487, 2013.
  21. T. H. Kil, S. H. Lee, and N. I. Cho, "Single image dehazing based on reliability map of dark channel prior," Proc. of International Conf. on Image Processing (ICIP), pp. 882-885, 2013