A Robust Object Tracking Method Using Structural Similarity in Daubechies Complex Wavelet Domain

  • Anand Singh Jalal
  • Uma Shanker Tiwary
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)


Many of the existing algorithms for object tracking that are based on spatial domain features, fail in the presence of illumination variation or change in appearance or pose or in the presence of noise. To overcome these problems, in this paper, we have proposed a new method of object tracking using structural similarity index in complex wavelet transform domain, which is approximately shift-invariant. The reference object in the initial frame is modeled by a feature vector in terms of the coefficients of Daubechies complex wavelet transform. A similarity measure based on structural similarity index is used to find the object in the current frame. The advantage of using structural similarity index in complex wavelet domain is that it allows small spatial translations, rotations and scaling changes, which are depicted in fig. 1. Experimental results illustrate that the proposed algorithm has good performance in noisy video with significant variations in object’s pose and illumination. The search for the candidate subframe is made fast by using the motion prediction algorithm.


Object Tracking Ground Truth Data Complex Wavelet Structural Similarity Index Spatial Translation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Yilmaz, A., Javed, O., Shah, M.: Object Tracking: A Survey. ACM Journal of Computing Surveys 38(4) (2006)Google Scholar
  2. 2.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based Object Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 564–575 (2003)CrossRefGoogle Scholar
  3. 3.
    Khansari, M., Rabiee, H.R., Asadi, M., Ghanbari, M.: Object Tracking in Crowded Video Scenes based on the Undecimated Wavelet Features and Texture Analysis. EURASIP Journal on Advances in Signal Processing, Article ID 243534 (2008)Google Scholar
  4. 4.
    Selsnick, I.W., Baraniuk, R.G., Kingsbury, N.: The Dual-Tree Complex Wavelet Transform. IEEE Signal Processing Magazine, 123–151 (November 2005)Google Scholar
  5. 5.
    Khare, A., Tiwary, U.S.: Symmetric Daubechies Complex Wavelet Transform and its Application to Denoising and Deblurring. WSEAS Transactions on Signal Processing 2(5), 738–745 (2006)Google Scholar
  6. 6.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image Quality Assessment: from Error Visibility to Structural Similarity. IEEE Trans. on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  7. 7.
    Wang, Z., Simoncelli, E.P.: Translation Insensitive Image Similarity in Complex Wavelet Domain. In: Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Philadelphia, PA, vol. 2, pp. 573–576 (2005)Google Scholar
  8. 8.
    Jalal, A.S., Tiwary, U.S.: A Robust Object Tracking Method for Noisy Video using Rough Entropy in Wavelet Domain. In: Proceedings of the International Conference Intelligent Human Computer Interaction, pp. 113–121. Springer, India (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Anand Singh Jalal
    • 1
  • Uma Shanker Tiwary
    • 1
  1. 1.Indian Institute of Information TechnologyAllahabadIndia

Personalised recommendations