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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 6 — Feb. 20, 2014
  • pp: 1181–1190

Detecting and tracking moving objects in long-distance imaging through turbulent medium

Eli Chen, Oren Haik, and Yitzhak Yitzhaky  »View Author Affiliations

Applied Optics, Vol. 53, Issue 6, pp. 1181-1190 (2014)

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The challenge of detecting and tracking moving objects in imaging throughout the atmosphere stems from the atmospheric turbulence effects that cause time-varying image shifts and blur. These phenomena significantly increase the miss and false detection rates in long-range horizontal imaging. An efficient method was developed, which is based on novel criteria for objects’ spatio-temporal properties, to discriminate true from false detections, following an adaptive thresholding procedure for foreground detection and an activity-based false alarm likeliness masking. The method is demonstrated on significantly distorted videos and compared with state of the art methods, and shows better false alarm and miss detection rates.

© 2014 Optical Society of America

OCIS Codes
(010.1330) Atmospheric and oceanic optics : Atmospheric turbulence
(100.3008) Image processing : Image recognition, algorithms and filters
(100.4999) Image processing : Pattern recognition, target tracking
(010.7295) Atmospheric and oceanic optics : Visibility and imaging

ToC Category:
Image Processing

Original Manuscript: October 18, 2013
Revised Manuscript: January 14, 2014
Manuscript Accepted: January 14, 2014
Published: February 20, 2014

Virtual Issues
Vol. 9, Iss. 4 Virtual Journal for Biomedical Optics

Eli Chen, Oren Haik, and Yitzhak Yitzhaky, "Detecting and tracking moving objects in long-distance imaging through turbulent medium," Appl. Opt. 53, 1181-1190 (2014)

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  1. W. Hu, T. Tan, L. Wang, and S. Maybank, “A survey on visual surveillance of object motion and behaviors,” IEEE Trans. Syst., Man, Cybern. C Appl. Rev. 34, 334–352 (2004).
  2. Y. Dedeoglu, Moving Object Detection, Tracking and Classication for Smart Video Surveillance (Bilkent University, 2004).
  3. O. Haik and Y. Yitzhaky, “Effects of image restoration on automatic acquisition of moving objects in thermal video sequences degraded by the atmosphere,” Appl. Opt. 46, 8562–8572 (2007). [CrossRef]
  4. 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).
  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). [CrossRef]
  6. A. Elkabetz and Y. Yitzhaky, “Background modeling for moving object detection in long-distance imaging through turbulent medium,” Appl. Opt. (2014), to be published.
  7. G. Baldini, P. Campadelli, D. Cozzi, and R. Lanzarotti, “A simple and robust method for moving target tracking,” in Proceedings of IASTED International Conference on Signal Processing, Pattern Recognition and Applications, (ACTA, 2012), pp. 108–112.
  8. A. Elgammal, D. Harwood, and L. Davis, “Non-parametric model for background subtraction,” in Proceedings of 6th European Conference on Computer Vision, Dublin, Ireland, Vol. 2 (Springer, 2000), pp. 751–767.
  9. O. Barnich and M. Van Droogenbroeck, “ViBe: a universal background subtraction algorithm for video sequences,” IEEE Trans. Image Process. 20, 1709–1724 (2011). [CrossRef]
  10. C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 1999).
  11. E. Chen, O. Haik, and Y. Yitzhaky, “Classification of thermal moving objects in atmospherically degraded video,” Opt. Eng. 51, 101710 (2012). [CrossRef]
  12. S. Cheung and C. Kamath, “Robust techniques for background subtraction in urban traffic video,” Proc. SPIE 5308, 881–892 (2004). [CrossRef]
  13. N. Lu, J. Wang, Q. H. Wu, and L. Yang, “An improved motion detection method for real-time surveillance,” IAENG Int. J. Comput. Sci. 35, 1 (2008).
  14. L. Zhang and Y. Liang, “Motion human detection based on background subtraction,” in Second International Workshop on Education Technology and Computer Science (IEEE, 2010).
  15. R. C. Ganzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, 2008), Chap. 9.
  16. N. S. Kopeika, A System Engineering Approach to Imaging, 2nd ed. (SPIE, 1998), Chap. 15.
  17. M. S. Belen’kii, J. M. Stewart, and P. Gillespie, “Turbulence-induced edge image waviness: theory and experiment,” Appl. Opt. 40, 1321–1328 (2001). [CrossRef]
  18. X. Zhu and J. M. Kahn, “Free-space optical communication through atmospheric turbulence channels,” IEEE Trans. Commun. 50, 1293–1300 (2002). [CrossRef]
  19. O. Oreifej, L. Xin, and M. Shah, “Simultaneous video stabilization and moving object detection in turbulence,” http://vision.eecs.ucf.edu/projects/Turbulence/TheeWayDec.zip .
  20. O. Barnich and M. Van Droogenbroeck, “ViBe: a universal background subtraction algorithm for video sequences,” http://hdl.handle.net/2268/145853 .
  21. Online Resource 1: http://www.ee.bgu.ac.il/~itzik/DetectTrackTurb/ .
  22. D. M. W. Powers, “Evaluation: from precision, recall and F-factor to ROC, informedness, markedness and correlation,” J. Mach. Learn. Technol. 2, 37–63 (2011).

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