KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Fast Video Fire Detection Using Luminous Smoke and Textured Flame Features

  • Ince, Ibrahim Furkan (Department of Electrical and Electronics Engineering, Kyungsung University) ;
  • Yildirim, Mustafa Eren (Faculty of Engineering and Natural Sciences, Bahcesehir University) ;
  • Salman, Yucel Batu (Faculty of Engineering and Natural Sciences, Bahcesehir University) ;
  • Ince, Omer Faruk (Department of Electrical and Electronics Engineering, Kyungsung University) ;
  • Lee, Geun-Hoo (R&D Laboratory, Hanwul Multimedia Communication Co. Ltd.) ;
  • Park, Jang-Sik (Department of Electrical and Electronics Engineering, Kyungsung University)
  • Received : 2016.01.28
  • Accepted : 2016.10.31
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this article, a video based fire detection framework for CCTV surveillancesystems is presented. Two novel features and a novel image type with their corresponding algorithmsareproposed for this purpose. One is for the slow-smoke detection and another one is for fast-smoke/flame detection. The basic idea is slow-smoke has a highly varying chrominance/luminance texture in long periods and fast-smoke/flame has a highly varying texture waiting at the same location for long consecutive periods. Experiments with a large number of smoke/flame and non-smoke/flame video sequences outputs promising results in terms of algorithmic accuracy and speed.

Keywords

References

  1. E. Cetin, K. Dimitropoulos, B. Gouverneur, N. Grammalidis, O. Günay, Y. H. Habiboglu, B. U. Toreyin, and S. Verstockt, "Video fire detection - Review," Digit. Signal Process, vol. 23, no. 6, pp.1827-1843, December, 2013. https://doi.org/10.1016/j.dsp.2013.07.003
  2. K. Dimitropoulos, O. Gunay, K. Kose, F. Erden, F. Chaabene, F. Tsalakanidou, N. Grammalidis, and E. Cetin, "Flame detection for video-based early fire warning for the protection of cultural heritage," in Proc. of the 4th Int. Conf. on Progress in Cultural Heritage Preservation, pp.378-387, October 29-November 3, 2012.
  3. B. Ko, J. Park, and J.Y. Nam, "Spatiotemporal bag-of-features for early wildfire smoke detection," Image Vision Computing,vol. 31, no.10, pp.786-795, October, 2013. https://doi.org/10.1016/j.imavis.2013.08.001
  4. H. Tian, W. Li, L. Wang, and P. Ogunbona, "A Novel Video-Based Smoke Detection Method Using Image Separation," in Proc. of the IEEE Int. Conf. on Multimedia and Expo, pp. 532-537, July 9-13, 2012.
  5. W. Yu, H. Ning, and L-Q. Juan, "A Smoke Detection Algorithm Based on Discrete Wavelet Transform and Correlation Analysis," in Proc. of the 4th Int. Conf. on Multimedia Information Networking and Security, pp.281-284, November 2-4, 2012.
  6. Feiniu 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, December, 2012. https://doi.org/10.1016/j.patcog.2012.06.008
  7. S. Calderara, P. Piccinini, and R. Cucchiara,"Vision based smoke detection system using image energy and color information," Machine Vision Applications,vol. 22, no. 4, pp.705-719, July, 2011. https://doi.org/10.1007/s00138-010-0272-1
  8. C. Long, J. Zhao, S. Han, L. Xiong, Z. Yuan, J. Huang, and W. Gao, "Transmission: a new feature for computer vision based smoke detection," in Proc. of the Int.Conf.on Artificial Intelligence and Computational Intelligence, pp.389-396, October 23-24, 2010.
  9. Dong-Keun Kim, "Smoke detection using boundary growing and moments," in Proc. of the Int. Conf. onHybrid Information Technology, pp.430-433, August 27-29, 2009.
  10. Feiniu 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, May, 2008. https://doi.org/10.1016/j.patrec.2008.01.013
  11. S. Calderara, P. Piccinini, and R. Cucchiara, "Smoke detection in video surveillance: a MoG model in the wavelet domain," in Proc. of the 6th Int. Conf. onComputer visionsystems, pp. 119-128, May 12-15, 2008.
  12. Truong Xuan Tung and Jong-Myon Kim,"An effective four-stage smoke-detection algorithm using video images for early fire-alarm systems," Fire Safety Journal, vol.46, no.5, pp.276-282, July,2011. https://doi.org/10.1016/j.firesaf.2011.03.003
  13. Hearst, M. A., Dumais, S. T., Osman, E., Platt, J., and B. Scholkopf, "Support vector machines," IEEE Intelligent Systems and their Applications, vol. 13, no.4, pp. 18-28, August, 1998. https://doi.org/10.1109/5254.708428
  14. Bilkent Video Dataset, Accessed June 6 2016.
  15. A. Vincitore, H. Wang, A. Finn, and O. Erdinc, "Spatial-temporal structural and dynamics features for Video Fire Detection," in Proc. of the IEEE Workshop on Applications of Computer Vision, pp.513-519, January 15-17, 2013.
  16. Truong Xuan Tung and Jong-Myon Kim, "Fire flame detection in video sequences using multi-stage pattern recognition techniques," Engineering Applications of Artificial Intelligence, vol. 25, no. 7, pp. 1365-1372, October, 2012. https://doi.org/10.1016/j.engappai.2012.05.007
  17. A. Ravichandran and S. Soatto, "Long-Range spatio-temporal modeling of video with application to fire detection," in Proc. of the 12th European Conf. on Computer Vision, pp. 329-342, October 7-13, 2012.
  18. Truong Xuan Tung and Jong-Myon Kim, "Fire detection with video using fuzzy c-means and back-propagation neural network," in Proc. of the 8th Int.Conf.on Advances in Neural Networks, pp.373-380, May 29-June 1, 2011.
  19. T.X. Truong, Y. Kim, J.M. Kim, "Fire Detection in Video Using Genetic-Based Neural Networks," in Proc. of the Int. Conf. on Information Science and Applications, pp. 1-5, April 26-29, 2011.
  20. Ishita Chakraborty and Tanoy Kr. Paul, "A Hybrid Clustering Algorithm for Fire Detection in Video and Analysis with Color Based Thresholding Method," in Proc. of the Int. Conf. onAdvances in Computer Engineering, pp.277-280, June 20-21, 2010.
  21. P.V.K. Borges, E. Izquierdo, "A Probabilistic Approach for Vision-Based Fire Detection in Videos," IEEE Transactions on Circuit and Systems for Video Technology, vol. 20, no.5, pp. 721-731, May, 2010. https://doi.org/10.1109/TCSVT.2010.2045813
  22. Feiniu Yuan, "An integrated fire detection and suppression system based on widely available video surveillance," Machine Vision Applications, vol. 21, no.6, pp.941-948, October, 2010. https://doi.org/10.1007/s00138-010-0276-x
  23. T. Celik, H. Demirel, H. Ozkaramanli, and M. Uyguroglu, "Fire detection using statistical color model in video sequences," Journal of Visual Communication and Image Representation, vol. 18, no. 2, pp. 176-185, April, 2007. https://doi.org/10.1016/j.jvcir.2006.12.003
  24. Jiang, B., Lu, Y., Li, X., and Lin, L., "Towards a solid solution of real-time fire and flame detection," Multimedia Tools and Applications, vol.74, no.3, pp. 689-705, February, 2015. https://doi.org/10.1007/s11042-014-2106-z
  25. H. Bay, A. Ess, T. Tuytelaars and L.V. Gool, "Speeded-up robust features (SURF)," Computer Vision and Image Understanding, vol. 110, no.3, pp. 346-359, June, 2008. https://doi.org/10.1016/j.cviu.2007.09.014
  26. Y. Habiboglu, H., Gunay, O., and A.E. Cetin, "Covariance matrix-based fire and flame detection method in video," Machine Vision and Applications, vol.23, no.6, pp.1103-1113, November, 2012. https://doi.org/10.1007/s00138-011-0369-1
  27. I.F. Ince, J.K. Do, G.Y. Kim and J.S. Park, "Patch-wise periodical re-occurrence analysis of motion for real-time video fire detection," in Proc. of the IEEE Int. Conf. on Industrial Technology, pp.651-654, February 26-March 1, 2014.
  28. Ince, I.F., Kim, G.Y., Lee, G.H., Park, J.S., "Patch-wise periodical correlation analysis of histograms for real-time video smoke detection," in Proc. of the IEEE Int. Conf. on Industrial Technology, pp.655-658, February 26-March 1, 2014.
  29. N. McFarlane, C. Schofield, "Segmentation and Tracking of Piglets in Images," Machine Vision and Applications, vol. 8, no.3, pp. 187-193, May, 1995. https://doi.org/10.1007/BF01215814
  30. Robert G. Keys, "Cubic convolution interpolation for digital image processing," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 29, no.6, pp. 1153-1160, December, 1981. https://doi.org/10.1109/TASSP.1981.1163711
  31. OpenCV: Geometric Image Transformations - OpenCV Documentation, Accessed June 6, 2016.
  32. cvBlob - Blob Library for OpenCV, Accessed June 6, 2016.
  33. Feiniu Yuan, "Video-based smoke detection with histogram sequence of LBP and LBPV pyramids," Fire Safety Journal, vol.46, no.3, pp.132-139, April, 2011. https://doi.org/10.1016/j.firesaf.2011.01.001
  34. W.S. Qureshi, M. Ekpanyapong, M.N. Dailey, "QuickBlaze: Early Fire Detection Using a Combined Video Processing Approach," Fire Technology, vol.52, no.5, pp.1293-1317, September, 2016. https://doi.org/10.1007/s10694-015-0489-7
  35. Y. Wang, T. W. Chua, R. Changand N. T. Pham, "Real-time smoke detection using texture and color features," in Proc of the 21st Int. Conf. On PatternRecognition, pp. 1727-1730, November 11-15, 2012.
  36. S. Wang, Y. He, J.J. Zou, D. Zhou, J. Wang, "Earlysmokedetection in video usingswayinganddiffusionfeature," Journal of IntelligentandFuzzySystems, vol. 26, no.1, pp. 267-275, January, 2014.
  37. O. Gunay, K. Tasdemir, B. U. Toreyinand A. E. Cetin, "Video basedwildfiredetection at night," Fire SafetyJournal, vol.44, no.6, pp.860-868, August, 2009. https://doi.org/10.1016/j.firesaf.2009.04.003
  38. Z. G. Liu, Y. Yangand X. H. Ji,"Flamedetectionalgorithmbased on a saliency detection technique and the uniform local binary pattern in theYCbCrcolorspace," Signal, Image and Video Processing, vol.10, no.2, pp. 277-284, February, 2016. https://doi.org/10.1007/s11760-014-0738-0