Skip to main content
SpringerLink
Log in

Fire smoke detection algorithm based on motion characteristic and convolutional neural networks

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

It is a challenging task to recognize smoke from visual scenes due to large variations in the feature of color, texture, shapes, etc. The current detection algorithms are mainly based on single feature or fusion of multiple static features of smoke, which leads to low detection accuracy. To solve this problem, this paper proposes a smoke detection algorithm based on the motion characteristics of smoke and the convolutional neural networks (CNN). Firstly, a moving object detection algorithm based on background dynamic update and dark channel priori is proposed to detect the suspected smoke regions. Then, the features of suspected region is extracted automatically by CNN, on that the smoke identification is performed. Compared to previous work, our algorithm improves the detection accuracy, which can reach 99% in the testing sets. For the problem that the region of smoke is relatively small in the early stage of smoke generation, the strategy of implicit enlarging the suspected regions is proposed, which improves the timeliness of smoke detection. In addition a fine-tuning method is proposed to solve the problem of scarce of data in the training network. Also, the algorithm has good smoke detection performance by testing under various video scenes.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Carreira J, Sminchisescu C (2012) CPMC: automatic object segmentation using constrained parametric min-cuts. IEEE Trans Pattern Anal Mach Intell 34(7):1312–1328

  2. Chen T, Wu P, Chiou Y (2004) An early fire-detection method based on image processing. Proc Int Conf on Image Proc 4:1707–1710

  3. Chenebert A, Breckon T P, Gaszczak A (2011) A non-temporal texture driven approach to real-time fire detection. In: 2011 18th IEEE International Conference on Image Processing, 263(4). IEEE, Brussels, pp 1741–1744

  4. Dong C, Loy CC, He K, Tang X (2016) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295–307

  5. Frizzi S, Kaabi R, Bouchouicha M, Ginoux JM, Moreau E, Fnaiech F (2016) Convolutional neural network for video fire and smoke detection. In: Proceedings of the IEEE conference on Industrial Electronics Society. IECON, Florence. pp 877–882

  6. Fujiwara N, Terada K (2004) Extraction of a smoke region using fractal coding. IEEE International Symposium on Communications and Information Technology, 2004(2). IEEE, Sapporo, pp 659-662

  7. Genovese A, Labati RD, Piuri V, Scotti F (2011) Wildfire smoke detection using computational intelligence techniques. In: IEEE International Conference on Computational Intelligence for Measurement Systems & Applications. IEEE, Ottawa. 43(4), pp 1–6

  8. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE Computer Society, Washington DC, pp 580–587

  9. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. In: proceedings of the 22nd ACM international conference on Multimedia. ACM, New York, pp 675–678

  10. Ko BC, Park JO, Nam JY (2013) Spatiotemporal bag-of-features for early wildfire smoke detection. Image Vis Comput 31(10):786–795

  11. Kolesov I, Karasev P, Tannenbaum A, Haber E (2010) Fire and smoke detection in video with optimal mass transport based optical flow andneural networks. In: Proceedings of International Conference on Image Processing, 119(5). IEEE Computer Society Press, Hong Kong, pp 761-764

  12. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 25(2):1097–1105

  13. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

  14. Levi G, Hassner T (2015) Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015. IEEE, Boston, pp 34–42

  15. Liang Zhao, Yan-Min Luo, Xiang-Yu Luo (2017) Based on dynamic backgroundupdate and dark channel prior offire smoke detection algorithm. Application Research of Computes 34(2):957–960

  16. Luo S (2013) State-of-art of video based smoke detection algorithms. J Image Graph 18(10):1225–1236

  17. Luo S, Yan C, Wu K, Zheng J (2015) Smoke detection based on condensed image. Fire Saf J 75(2015):23–35

  18. Sun Y, Wang X, Tang X (2014) Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014. IEEE, Columbus, pp 1891–1898

  19. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S , Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference oncomputer vision and pattern recognition, 2015. IEEE, Boston, pp 1–9

  20. Tao C, Zhang J, Wang P (2016) Smoke detection based on deep convolutional neural networks. In: Proceedings of the IEEE conference on Industrial Informatics-Computing Technology, Intelligent Technology (ICIICII), 2016. IEEE, Wuhan, pp 150–153

  21. Tian H, Li W, Ogunbona P, Nguyen D T (2011) Smoke detection in videos using non-redundant local binary pattern-based features. In: IEEE International Workshop on Multimedia Signal Processing, 2011. IEEE, Hangzhou, pp 1–4

  22. Tian H, Li W, Wang L, Ogunbona P (2012) A novel video-based smoke detection method using image separation. In: Proceedings of International Conference on Multimedia and Expo. IEEE Press, Melbourne. pp 532–537

  23. Toreyin BU, Dedeoglu Y, Cetin AE (2015) Wavelet based real-time smoke detection in video. In: Proceedings of 13th European Signal Processing Conference, Antalya, 2015. pp 1-4

  24. Uijlings JR, Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171

  25. Wang T, Liu Y, Xie ZP (2011) Flutter analysis based video smoke detection. J Electron Inf Technol 35(5):1024–1029

  26. Yan C, Zhang Y, Xu J, Dai F, Li L, Dai Q, Wu F (2014) A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21(5):573–576

  27. Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014) Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans Circuits Syst Video Technol 24(12):2077–2089

  28. Yan C, Zhang Y, Dai F, Wang X (2014) Parallel deblocking filter for HEVC on many-core processor. Electronics Letters 50(5):367–368

  29. Yan C, Zhang Y, Dai F, Zhang J, Li L, Dai Q (2014) Efficient parallel HEVC intra-prediction on many-core processor. Electron Lett 50(11):805–806

  30. Yu C, Fang J, Wang J, Wang Y (2010) Video fire Smokedetection using motion and color features. Fire Technol 46(3):651–663

  31. Yuan FN (2008) A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recogn Lett 29(7):925–932

  32. Yuan FN (2011) Video-based smoke detection with histogram sequence of LBP and LBPV pyramids. Fire Saf J 46(3):132–139

Download references

Author information

Authors and Affiliations

  1. College of Computer Science & Technology, Huaqiao University, Xiamen, 361021, China

    Yanmin Luo & Liang Zhao

  2. College of Mechanical Engineering & Automation, Huaqiao University, Xiamen, 361021, China

    Yanmin Luo

  3. College of Engineering, Huaqiao University, Quanzhou, Fujian, 362021, China

    Peizhong Liu & Detian Huang

Authors
  1. Yanmin Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peizhong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Detian Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanmin Luo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, Y., Zhao, L., Liu, P. et al. Fire smoke detection algorithm based on motion characteristic and convolutional neural networks. Multimed Tools Appl 77, 15075–15092 (2018). https://doi.org/10.1007/s11042-017-5090-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5090-2

Keywords

Search

Navigation