Forest Fire Visual Tracking with Mean Shift Method and Gaussian Mixture Model

  • Bo Cai
  • Lu Xiong
  • Jianhui ZhaoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 670)


Forest fire region from surveillance video is non-rigid object with varying size and shape. It is complex and thus difficult to be tracked automatically. In this paper, a new tracking algorithm is proposed by mean shift method and Gaussian mixture model. Based on moment features of mean shift algorithm, the size adaptive tracking window is employed to reflect size and shape changes of object in real time. Meanwhile, the Gaussian mixture model is utilized to obtain the probability value of belonging to fire region for each pixel. Then, the probability value is used to update the weighting parameter of each pixel in mean shift algorithm, which reduces the weight of non-fire pixels and increases the weight of fire pixels. With these techniques, the mean shift algorithm can converge to forest fire region faster and accurately. The presented algorithm has been tested on real monitoring video clips, and the experimental results prove the efficiency of our new method.


Non-rigid object Tracking algorithm Mean shift method Gaussian mixture model 


  1. 1.
    Liu Z.G., Yang Y., Ji X.H. Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space. International Journal of Signal, Image and Video Processing, 2016, 10, 277–284.Google Scholar
  2. 2.
    Zhang H.J., Zhang N., Xiao N.F. Fire detection and identification method based on visual attention mechanism. Optik, 2015, 126, 5011–5018.Google Scholar
  3. 3.
    Ye W., Zhao J., Wang S., Wang Y., Zhang D., Yuan Z. Dynamic Texture Based Smoke Detection Using Surfacelet Transform and HMT Model. Fire Safety Journal, 2015, 73, 91–101.Google Scholar
  4. 4.
    Roberto R.R. Remote detection of forest fires from video signals with classifiers based on K-SVD learned dictionaries. Engineering Applications of Artificial Intelligence, 2014, 33, 1–11.Google Scholar
  5. 5.
    Yuan F.N., Fang Z.J., Wu S.Q., Yang Y., Fang Y.M. Real-time image smoke detection using staircase searching-based dual threshold AdaBoost and dynamic analysis. IET Image Processing, 2015, 9(10), 849–856.Google Scholar
  6. 6.
    Kosmas D., Panagiotis B., Nikos G. Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(2), 339–351.Google Scholar
  7. 7.
    Zhang Z.J., Shen T., Zou J.H. An Improved Probabilistic Approach for Fire Detection in Videos. Fire Technology, 2014, 50, 745–752.Google Scholar
  8. 8.
    Zhao Y.Q., Tang G.Z., Xu M.M. Hierarchical detection of wildfire flame video from pixel level to semantic level. Expert Systems with Applications, 2015, 42, 4097–4014.Google Scholar
  9. 9.
    Miguel A. Carreira-Perpinan. Gaussian Mean-shift is an EM Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(5), 767–776.Google Scholar
  10. 10.
    Ido Leichter, Michael Lindenbaum. Mean Shift tracking with multiple reference color histograms. Computer Vision and Image Understanding, 2010, 114, 400–408.Google Scholar
  11. 11.
    Chunhua Shen. Fast Global Kernel Density Mode Seeking: Applications to Localization and Tracking. IEEE Transactions on Image Processing, 2007, 16(5), 1457–1469.Google Scholar
  12. 12.
    K. Fukunaga and L.D. Hostetler. The Estimation of the gradient of a density function with applications in pattern recognition. IEEE Transactions on Information Theory, 1975, 21(1), 32–40.Google Scholar
  13. 13.
    Y. Cheng. Mean Shift, Mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8), 790–799.Google Scholar
  14. 14.
    D. Comaniciu, V. Ramesh and P. Meer. Kernel-based object tracking. IEEE transactions on Pattern Analysis and Machine Intelligence, 2003, 25(4), 564–575.Google Scholar
  15. 15.
    D. Comaniciu, V. Ramesh and P. Meer. Real-time tracking of non-rigid objects using mean shift. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Hilton Head, SC, Volume II, June 2000, 142–149.Google Scholar
  16. 16.
    C. Biemacki, G. Celeux, G. Govaert. Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22, 719–725.Google Scholar
  17. 17.
    Heng-Chao Yan, Jun-Hong Zhou, Chee Khiang Pang. Gaussian Mixture Model Using Semisupervised Learning for Probabilistic Fault Diagnosis Under New Data Categories. IEEE Transactions on Instrumentation and Measurement, 2017, 66(4), 723–733.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of ComputerWuhan UniversityWuhanChina

Personalised recommendations