Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Algorithm for Detection of Fire Smoke in a Video Based on Wavelet Energy Slope Fitting

  • Zhang, Yi (Information Center, Jiangsu University of Technology) ;
  • Wang, Haifeng (Information Center, Jiangsu University of Technology) ;
  • Fan, Xin (School of Automobile and Traffic Engineering, Jiangsu University of Technology)
  • Received : 2018.06.18
  • Accepted : 2019.01.14
  • Published : 2020.06.30
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Abstract

The existing methods for detection of fire smoke in a video easily lead to misjudgment of cloud, fog and moving distractors, such as a moving person, a moving vehicle and other non-smoke moving objects. Therefore, an algorithm for detection of fire smoke in a video based on wavelet energy slope fitting is proposed in this paper. The change in wavelet energy of the moving target foreground is used as the basis, and a time window of 40 continuous frames is set to fit the wavelet energy slope of the suspected area in every 20 frames, thus establishing a wavelet-energy-based smoke judgment criterion. The experimental data show that the algorithm described in this paper not only can detect smoke more quickly and more accurately, but also can effectively avoid the distraction of cloud, fog and moving object and prevent false alarm.

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References

  1. X. Sun, L. Sun, Y. Liu, and Y. Huang, "Research of forest fire smoke recognition method based on gray bit plane technology," International Journal of Smart Home, vol. 9, no. 4, pp. 57-64, 2015.
  2. Y. Luo, L. Zhao, P. Liu, and D. Huang, "Fire smoke detection algorithm based on motion characteristic and convolutional neural networks," Multimedia Tools and Applications, vol. 77, no. 12, pp. 15075-15092, 2018. https://doi.org/10.1007/s11042-017-5090-2
  3. Y. Jia, J. Yuan, J. Wang, J. Fang, Q. Zhang, and Y. Zhang, "A saliency-based method for early smoke detection in video sequences," Fire Technology, vol. 52, no. 5, pp. 1271-1292, 2016. https://doi.org/10.1007/s10694-014-0453-y
  4. C. E. Prema, S. S. Vinsley, and S. Suresh, "Multi feature analysis of smoke in YUV color space for early forest fire detection," Fire Technology, vol. 52, no. 5, pp. 1319-1342, 2016. https://doi.org/10.1007/s10694-016-0580-8
  5. A. Filonenko, D. C. Hernandez, and K. H. Jo, "Fast smoke detection for video surveillance using CUDA," IEEE Transactions on Industrial Informatics, vol. 14, no. 2, pp. 725-733, 2017. https://doi.org/10.1109/tii.2017.2757457
  6. A. Singh, B. Singh, B. Grover, G. Bhutani, and A. Sharma, "FSIT: fire safety in trains," International Journal of Smart Home, vol. 10, no. 6, pp. 61-70, 2016.
  7. S. Luo, C. Yan, K. Wu, and J. Zheng, "Smoke detection based on condensed image," Fire Safety Journal, vol. 75, pp. 23-35, 2015. https://doi.org/10.1016/j.firesaf.2015.04.002
  8. C. Yuan, Z. Liu, and Y. Zhang, "Learning-based smoke detection for unmanned aerial vehicles applied to forest fire surveillance," Journal of Intelligent & Robotic Systems, vol. 93, no. 1-2, pp. 337-349, 2019. https://doi.org/10.1007/s10846-018-0803-y
  9. Y. Zhao, Z. Zhou, and M. Xu, "Forest fire smoke video detection using spatiotemporal and dynamic texture features," Journal of Electrical and Computer Engineering, vol. 2015, article no.706187, 2015
  10. D. K. Appana, R. Islam, S. A. Khan, and J. M. Kim, "A video-based smoke detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems," Information Sciences, vol. 418, pp. 91-101, 2017. https://doi.org/10.1016/j.ins.2017.08.001
  11. B. U. Toreyin, Y. Dedeoglu, and A. E. Cetin, "Wavelet based real-time smoke detection in video," in Proceedings of 2005 13th European Signal Processing Conference, Antalya, Turkey, 2005, pp. 1-4.
  12. N. Fujiwara and K. Terada, "Extraction of a smoke region using fractal coding," in Proceedings of IEEE International Symposium on Communications and Information Technology, Sapporo, Japan, 2004, pp. 659-662. IEEE.
  13. B. L. Zhou, Y. L. Song, and M. H. Yu, "Fire smoke detection algorithm based on image disposal," Fire Science and Technology, vol. 35, no. 3, pp. 390-393, 2016.
  14. L. Wang, A. G. Li, X. N. Wang, and Y. F. Yu, "An early fire smoke detection method based on multi-features fusion," Journal of Dalian Maritime University, vol. 40, no. 1, pp. 97-100, 2014.
  15. S. Wang, Y. He, J. J. Zou, D. Zhou, and J. Wang, "Early smoke detection in video using swaying and diffusion feature," Journal of Intelligent & Fuzzy Systems, vol. 26, no. 1, pp. 267-275, 2014. https://doi.org/10.3233/IFS-120735
  16. F. Yuan, "A fast accumulative motion orientation model based on integral image for video smoke detection," Pattern Recognition Letters, vol. 29, no. 7, pp. 925-932, 2008. https://doi.org/10.1016/j.patrec.2008.01.013
  17. F. N. Yuan, Y. M. Zhang, S. X. Liu, C. Yu, and S. Shen, "Video smoke detection based on accumulation and main motion orientation," Journal of Image and Graphics, vol. 13, no. 4, pp. 808-813, 2018.
  18. F. Yuan, "A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with AdaBoost for video smoke detection," Pattern Recognition, vol. 45, no. 12, pp. 4326-4336, 2012. https://doi.org/10.1016/j.patcog.2012.06.008
  19. C. Yu, J. Fang, J. Wang, and Y. Zhang, "Video fire smoke detection using motion and color features," Fire Technology, vol. 46, no. 3, pp. 651-663, 2010. https://doi.org/10.1007/s10694-009-0110-z
  20. Z. Zhou, Y. Zhao, Y. Tang, and Y. Zhang, "Segmentation of forest fire video smoke region based on the temporal-spatial features," Journal of Chinese Agricultural Mechanization, vol. 37, no. 2, pp. 196-199, 2016.
  21. L. Zhao, Y. M. Luo, and X. Y. Luo, "Based on dynamic background update and dark channel prior of fire smoke detection algorithm," Application Research of Computes, vol. 34, no. 2, pp. 957-960, 2017.
  22. F. Nazary and A. Fotouhi, "Toward a smoke detection system for early fire alarming based on video processing and fuzzy reasoning," International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, vol. 25, no. 1, pp. 41-48, 2017.
  23. F. Yuan, Z. Fang, S. Wu, Y. Yang, and Y. Fang, "Real-time image smoke detection using staircase searchingbased dual threshold AdaBoost and dynamic analysis," IET Image Processing, vol. 9, no. 10, pp. 849-856, 2015. https://doi.org/10.1049/iet-ipr.2014.1032
  24. Z. G. Liu, Y. Yang, and X. H. Ji, "Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space," Signal, Image and Video Processing, vol. 10, no. 2, pp. 277-284, 2016. https://doi.org/10.1007/s11760-014-0738-0
  25. Z. Zhou and Y. Q. Zhao, "A new smoke detection method of forest fire video with color and flutter," Proceedings of the 2015 Chinese Intelligent Automation Conference. Heidelberg: Springer, 2015, pp. 151-161.
  26. F. Yuan, X. Xia, L. Shi, H. Li, and G. Li, "Non-linear dimensionality reduction and gaussian process based classification method for smoke detection," IEEE Access, vol. 5, pp. 6833-6841, 2017. https://doi.org/10.1109/ACCESS.2017.2697408
  27. S. Wang, Y. He, H. Yang, K. Wang, and J. Wang, "Video smoke detection using shape, color and dynamic features," Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 305-313, 2017. https://doi.org/10.3233/JIFS-161605