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Multimedia Tools and Applications

, Volume 71, Issue 3, pp 1179–1199 | Cite as

Detecting and tracking dim small targets in infrared image sequences under complex backgrounds

  • Ying Li
  • Shi Liang
  • Bendu Bai
  • David Feng
Article

Abstract

This paper presents a unified framework for automatically detecting and tracking dim small targets in infrared (IR) image sequence under complex backgrounds. Firstly, the variance weighted information entropy (variance WIE) followed by a region growing technique is introduced to segment the candidate targets in a single-frame IR image after background suppression. Then the pipeline filter is used to verify the real targets. The position and the size of the detected target are then obtained to initialize the tracking algorithm. Secondly, we adopt an improved local binary pattern (LBP) scheme to represent the target texture feature and propose a joint gray-texture histogram method for a more distinctive and effective target representation. Finally, target tracking is accomplished by using the mean shift algorithm. Experimental results indicate that the proposed method can effectively detect the dim small targets under complex backgrounds and has better tracking performance compared with the gray histogram based tracking methods such as the mean shift and the particle filtering.

Keywords

Dim small target detection and tracking Variance weighted information entropy (variance WIE) Local binary pattern (LBP) Mean shift 

Notes

Acknowledgments

We are very grateful to the anonymous reviewers for their constructive comments and suggestions that help improve the quality of this manuscript. We also appreciate the providers of the test sequences. This works was supported by the National Natural Science Foundation of China (No. 60873086), and the Aeronautics Science Foundation of China (No. 2011ZD53049).

References

  1. 1.
    Bai X, Zhou F, Jin F (2010) Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter. Signal Process 90:1643–1654CrossRefzbMATHGoogle Scholar
  2. 2.
    Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 17(8):790–799CrossRefGoogle Scholar
  3. 3.
    Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577CrossRefGoogle Scholar
  4. 4.
    Deng H, Liu JG (2011) Infrared small target detection based on the self-information map. Infrared Phys Technol 54(2):100–107CrossRefGoogle Scholar
  5. 5.
    Fukunaga K, Hostetler HD (1975) The estimation of the gradient of a density function with applications in pattern recognition. IEEE Trans Inf Theory 21(1):32–40CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663CrossRefMathSciNetGoogle Scholar
  7. 7.
    Guo Z, Zhang L, Zhang D (2010) Rotation invariant texture classification using LBP Variance (LBPV) with global matching. Pattern Recognit 43(3):706–719CrossRefzbMATHGoogle Scholar
  8. 8.
    Haritaoglu I, Flickner M (2012) Efficient tracking using a robust motion estimation technique. Multimed Tools Appl 58:1–16CrossRefGoogle Scholar
  9. 9.
    Huang K, Mao X (2010) Detectability of infrared small targets. Infrared Phys Technol 53(3):208–217CrossRefGoogle Scholar
  10. 10.
    Kerekes R (2009) Enhanced video-based target detection using multi-frame correlation filtering. IEEE Trans Aerosp Electron Syst 45(1):289–307CrossRefGoogle Scholar
  11. 11.
    Laura SL, Erik LM (2012) Distribution fields for tracking. In: Proc. of the IEEE Comput Vis Pattern Recogn, pp 1910–1917Google Scholar
  12. 12.
    Li Y, Mao XJ, Feng D, Zhang YN (2011) Fast and accuracy extraction of infrared target based on Markov random field. Signal Process 91:1216–1223CrossRefGoogle Scholar
  13. 13.
    Liu Z, Chen C, Shen X (2005) Detection of small objects in image data based on the non linear principal component analysis neural network. Opt Eng 44(9):1–9CrossRefzbMATHGoogle Scholar
  14. 14.
    Liu R, Liu E, Yang J, Zhang T, Wang F (2007) Infrared small target detection with kernel Fukunaga–Koontz transform. Meas Sci Technol 18:3025–3035CrossRefGoogle Scholar
  15. 15.
    Ning JF, Zhang Z, Wu CK (2009) Robust object tracking using joint color-texture histogram. Int J Pattern Recognit Artif Intell 23(7):1245–1263CrossRefGoogle Scholar
  16. 16.
    Nummiaro K, Koller-Meier E, Gool LV (2003) An adaptive color-based particle filter. Image Vis Comput 21(1):99–110CrossRefGoogle Scholar
  17. 17.
    Ojala T, Pietikäinen M, Mäenpä T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  18. 18.
    Ojala T, Valkealahti K, Oja E, Pietikäinen M (2001) Texture discrimination with multi-dimensional distributions of signed gray level differences. Pattern Recognit 34(3):727–739CrossRefzbMATHGoogle Scholar
  19. 19.
    OTCBVS benchmark dataset. [online] http://www.cse.ohio-state.edu/otcbvs-bench
  20. 20.
    Peng GH, Chen H, Wu Q (2011) Infrared small target detection under complex background. Adv Mater Res 346:615–619CrossRefGoogle Scholar
  21. 21.
    Polat E, Ozden M (2006) A nonparametric adaptive tracking algorithm based on multiple feature distributions. IEEE Trans Multimed 8:1156–1163CrossRefGoogle Scholar
  22. 22.
    Shao XP, Fan H, Lu GX, Xu J (2012) An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system. Infrared Phys Technol, Available online 15 June 2012Google Scholar
  23. 23.
    Soni T, Zeidler JR, Ku W (1993) Performance evaluation of 2D adaptive prediction filters for detection of small objects in image data. IEEE Trans Image Process 2(3):327–340CrossRefGoogle Scholar
  24. 24.
    Valtteri V, Pietikainen M (2007) Multi-object tracking using color, texture and motion. In: Proc. of the IEEE Comput Vis Pattern Recogn, pp 1–7Google Scholar
  25. 25.
  26. 26.
    Wang SP (2010) Adaptive feature selection for infrared object tracking. Wireless Communications Networking and Mobile Computing, pp 1–4Google Scholar
  27. 27.
    Wang P, Tian JW, Gao CQ (2009) Infrared small target detection using directional high pass filters based on LS-SVM. Electron Lett 45(3):156–158CrossRefGoogle Scholar
  28. 28.
    Wang JQ, Yagi Y (2006) Integrating shape and color features for adaptive real-time target tracking. In: Proc. of the IEEE Robotics and Biomimetics, pp 1–6Google Scholar
  29. 29.
    Yang L, Yang J, Yang K (2004) Adaptive detection for infrared small target under sea-sky complex background. Electron Lett 40(17):1083–1085CrossRefGoogle Scholar
  30. 30.
    Yang L, Zhou Y, Yang J, Chen L (2006) Variance WIE based infrared images processing. Electron Lett 42(15):857–859CrossRefGoogle Scholar
  31. 31.
    Yilmaz A, Shafique K, Shah M (2003) Target tracking in airborne forward looking infrared imagery. Image Vis Comput 21:623–635CrossRefGoogle Scholar
  32. 32.
    Zhang F, Li C, Shi L (2005) Detecting and tracking dim moving point target in IR image sequence. Infrared Phys Technol 46:323–328CrossRefGoogle Scholar
  33. 33.
    Zhang B, Zhang T, Zhang K, Cheng Z, Cao Z (2007) Adaptive rectification filter for detecting small IR targets. IEEE A&E Syst Mag 22(8):20–26CrossRefGoogle Scholar
  34. 34.
    Zhang T, Zuo Z, Yang W, Sun X (2007) Moving dim point target detection with three-dimensional wide-to-exact search directional filtering. Pattern Recognit Lett 28(2):246–253CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  2. 2.School of Telecommunication and Information EngineeringXi’an University of Posts and TelecommunicationsXi’anChina
  3. 3.School of Information Technology, J11University of SydneySydneyAustralia

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