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Tracking of Non-Rigid Object in Complex Wavelet Domain

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DOI: 10.4236/jsip.2011.22014    5,580 Downloads   8,993 Views   Citations

ABSTRACT

In this paper we have proposed an object tracking method using Dual Tree Complex Wavelet Transform (DTCxWT). The proposed method is capable of tracking the moving object in video sequences. The object is assumed to be deform-able under limit i.e. it may change its shape from one frame to another. The basic idea in the proposed method is to decompose the image into two components: a two dimensional motion and a two dimensional shape change. The motion component is factored out while the shape is explicitly represented by storing a sequence of two dimensional models. Each model corresponds to each image frame. The proposed method performs well when the change in the shape in the consecutive frames is small however the 2-D motion in consecutive frames may be large. The proposed algorithm is capable of handling the partial as well as full occlusion of the object.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

O. Prakash and A. Khare, "Tracking of Non-Rigid Object in Complex Wavelet Domain," Journal of Signal and Information Processing, Vol. 2 No. 2, 2011, pp. 105-111. doi: 10.4236/jsip.2011.22014.

References

[1] A. K. Jain, “Fundamentals of Digital Image Processing,” Prentice Hall of India Pvt. Ltd., New Delhi, 2001.
[2] S. Nigam and A. Khare, “Curvelet Transform Based Object Tracking,” Proceedings of IEEE International Conference on Computer and Communication Technologies, Allahabad, 17-19 September 2010, pp. 230-235.
[3] M. Khare, T. Patnaik and A. Khare, “Dual Tree Complex Wavelet Transform Based Video Object Tracking,” Com- munications in Computer and Information Science, Vol. 101, No. 2, 2010, pp. 281-286.
[4] H. Goszczynska, “A Method for Densitometric Analysis of Moving Object Tracking in Medical Images,” Machine Graphics & Vision International Journal, Vol. 17, No. 1, 2008.
[5] Z. M. Budimlija, M. Leckpammer, D. Popiolek, F. Fogt, M. Forenderick and R.Bieber, “Forensic Applications of Capture Laser Microdissections: Use in DNA-Based Parentage Testing and Plateform Validation,” Croatian Medical Journal, Vol. 46, No. 4, 2005, pp. 549-555.
[6] R. Anderson, N. Kingsbury and J. Fauqueur, “Coarse Level Object Recognition Using Interlevel Products of Complex Wavelets,” Proceedings of IEEE Conference on Image Processing, Genoa, September 2005.
[7] N. G. Kingsbury and J. F. A. Magarey, “Wavelet Transforms in Image Processing,” Proceedings of 1st European Conference on Signal Analysis and Prediction, Prague, 24-27 June 1997, pp. 23-34.
[8] I. W. Selesnick, R. G. Baraniuk and N. G. Kingsbury, “The Dual-Tree Complex Wavelet Transform,” IEEE Signal Processing Magazine, Vol. 22, No. 6, 2005, pp. 123-151. doi:10.1109/MSP.2005.1550194
[9] F. C. A. Fernandes, R. L. C. Spaendonck and C. S. Burrus, “A New Framework for Complex Wavelet Transform,” IEEE Transactions on Signal Processing, Vol. 51, No. 7, 2003, pp. 1825-1837. doi:10.1109/TSP.2003.812841
[10] A. A. Bharath and J. Ng, “A Steerable Complex Wavelet Construction and Its Applications to Image Denoising,” IEEE Transactions on Image Processing, Vol. 14, No. 7, 2005, pp. 948-959. doi:10.1109/TIP.2005.849295
[11] N. G. Kingsbury, “Shift Invariant Properties of the Dual-Tree Complex Wavelet Transform,” Proceedings of IEEE Conference on Acoustics, Speech and Signal Processing, Phoenix, Vol. 3, 16-19 March 1999.
[12] N. Kingsbury, “Rotation-Invariant Local Feature Matching with Complex Wavelets,” Proceedings of European Conference on Signal Processing, Florence, 4-8 September 2006.
[13] A. Khare and U. S. Tiwary, “Daubechies Complex Wave- let Transform Based Moving Object Tracking,” IEEE Symposium on Computational Intelligence in Image and Signal Processing, Honolulu, 1-5 April 2007, pp. 36-40.
[14] N. G. Kingsbury, “Image Processing with Complex Wavelets,” Philosophical Transactions of Royal Society London A, Special Issue for the Discussion Meeting on “Wavelets: The Key to Intermittent Information?” Vol. 357, No. 1760, 1999, pp. 2543-2560.
[15] N. G. Kingsbury, “The Dual-Tree Complex Wavelet Transform: A New Technique for Shift Invariance and Directional Filters,” Proceedings of 8th IEEE DSP Workshop, Bryce Canyon, August 1998.
[16] N. G. Kingsbury, “Complex Wavelets for Shift Invariant Analysis and Filtering of Signals,” Applied and Computational Harmonic Analysis, Vol. 10, No. 3, 2001, pp. 234-253. doi:10.1006/acha.2000.0343
[17] N. G. Kingsbury, “The Dual-Tree Complex Wavelet Transform: A New Efficient Tool for Image Restoration and Enhancement,” The 9th European Signal Processing Conference, Rhodes, September 1998.
[18] D. P. Huttenlocher, J. J. Noh and W. J. Rucklidge, “Tracking Non-Rigid Objects in Complex Scenes,” Proceedings of 4th International Conference on Computer Vision, Berlin, 11-14 May 1993, pp. 93-101.

  
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