Moving Target Detection Under Turbulence Degraded Visible and Infrared Image Sequences

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 703)

Abstract

The presence of atmospheric turbulence over horizontal imaging paths introduces time-varying perturbations and blur in the scene that severely degrade the performance of moving object detection and tracking systems of vision applications. This paper proposed a simple and efficient algorithm for moving target detection under turbulent media, based on adaptive background subtraction approach with different types of background models followed by adaptive global thresholding to detect foreground. This proposed method is implemented in MATLAB and tested on turbulence degraded video sequences. Further, this proposed method is also compared with state-of-the-art method published in the literature. The result shows that the detection performance by proposed algorithm is better. Further, the proposed method can be easily implemented in FPGA-based hardware.

Keywords

Moving object detection Imaging under turbulent media Performance metrics Background subtraction Computer vision and target detection algorithm 

References

  1. 1.
    Roggermann, M.C., Welsh, B, “ Imaging through turbulence”, Cap.3, CRC Press, USA, pp 57–115 (1996).Google Scholar
  2. 2.
    B. Fishbain, L. P. Yaroslavsky and I.A. Ideses, “Real time stabilization of long range observation system turbulent video”, J. Real Time Image Proc. 2, 11–22, 2007.CrossRefGoogle Scholar
  3. 3.
    Y. Benezeth, P.M. Jodoin, B. Emile, H. Laurent, and C. Rosenberger, “Comparative study of background subtraction algorithms”, J. Electron Imaging 19, 033003 (2010).Google Scholar
  4. 4.
    O. Haik and Y. Yitzhaky, “Effects of image registration on automatic acquisition of moving objects in thermal; video sequences cdegraded by atmosphere”, Appl Opt. 46, 8562–8572 (2007).CrossRefGoogle Scholar
  5. 5.
    O. Oreifej, L. Xin and M. Shah, “Simultaneous video stabilization and moving object detection in turbulence”, IEEE Trans. Pattern Anal. Mach. Intell. 35, 450–462 (2013).CrossRefGoogle Scholar
  6. 6.
    C. Stauffer and W. Grimson, “Adaptive background mixture models for real time tracking”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recoginition, pp 246–252, 1999.Google Scholar
  7. 7.
    G. Baldini, P. Campadelh, D. Cozzi, and R. Lanzarotti, “A simple and Robust method for moving target tracking”, in Proceedings of International Conference of Signal Processing, Pattern Recognition and Applications, (ACTA, 2012), 108–112.Google Scholar
  8. 8.
    E. Chen, O. Haik and Y. Yitzhaky, “Detecting and tracking moving objects in long distance imaging through turbulent medium”, Appl Opt. 53, 1181–1190 (2014).CrossRefGoogle Scholar
  9. 9.
    O. Barnich and M. Van Droogenbroeck, “ViBe a universal background subtraction algorithm for video sequences”, IEEE Trans. Image Process. 20, 1709–1724 (2011).Google Scholar
  10. 10.
  11. 11.
    S. Cheung and C. Kamath, “Robust techniques for background subtraction in urban traffic video”, Proc. SPIE 5308, 881–892. (2004).Google Scholar
  12. 12.
    Andrew Sobral, “A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos”, Computer Vision and Image Understanding, 4–21 (2014).CrossRefGoogle Scholar
  13. 13.
    Otsu N., “A threshold selection method from gray-level histograms”, IEEE Transactions on Systems, Man, and Cybemetics, Vol. 9, No. 1, 1979, pp. 62–66.MathSciNetCrossRefGoogle Scholar
  14. 14.
    R.C. Gonzalez and R.E. Woods, Digital Image Processing, 3rd Ed, (Prentice-Hall, 2008).Google Scholar
  15. 15.
    Faisal Bashir and Fatih Porikli, “Performance evaluation of object detection and tracking systems”, CVPR (2006).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electronic ScienceKurukshetra UniversityKurukshetraIndia
  2. 2.Instrumentation Research and Development EstablishmentDehradunIndia

Personalised recommendations