Curvelet transform-based technique for tracking of moving objects

Access Full Text

Curvelet transform-based technique for tracking of moving objects

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Published

This study provides an object tracking method in video sequences, which is based on curvelet transform. The wavelet transform has been widely used for object tracking purpose, but it cannot well describe curve discontinuities. We have used curvelet transform for tracking. Tracking is done using energy of curvelet coefficients in sequence of frames. The proposed method is simple and does not rely on any other parameter except curvelet coefficients. Compared with a number of schemes like Kalman filter, particle filter, Bayesian methods, template model, corrected background weighted histogram, joint colour texture histogram and covariance-based tracking methods, the proposed method extracts effectively the features in target region, which characterise better and represent more robustly the target. The experimental results validate that the proposed method improves greatly the tracking accuracy and efficiency than traditional methods.

Inspec keywords: curvelet transforms; video signal processing; feature extraction; object tracking

Other keywords: object tracking method; curvelet transform-based technique; curvelet coefficient; video sequence; moving object tracking; feature extraction

Subjects: Integral transforms in numerical analysis; Integral transforms in numerical analysis; Optical, image and video signal processing; Video signal processing

References

    1. 1)
    2. 2)
    3. 3)
      • Nguyen, Q.A., Robles-Kelly, A., Shen, C.: `Enhanced kernel-based tracking for monochromatic and thermographic video', Proc. IEEE Int. Conf. Video and Signal Based Surveillance, 2006, Sydney, Australia, p. 28–33.
    4. 4)
    5. 5)
      • Haritaoglu, I., Flickner, M.: `Detection and Tracking of Shopping groups in Stores', IEEE Conf. Computer Vision and Pattern Recognition, USA, 2001, p. 431–438.
    6. 6)
    7. 7)
    8. 8)
      • S. Nigam , A. Khare , C. Singh . (2011) Multifont Oriya character recognition using curvelet transform, Information Systems for Indian Languages, Communications in Computer and Information Science.
    9. 9)
    10. 10)
    11. 11)
      • E.J. Candès , D.L. Donoho , L.L. Schumaker . (1999) Curvelets – a surprisingly effective nonadaptive representation for objects with edges, Curves and surfaces.
    12. 12)
      • Khare, A., Tiwary, U.S.: `Daubechies complex wavelet transform based moving object tracking', IEEE Symp. on Computational Intelligence in Image and Signal Processing, 2007, Honolulu, HI, p. 36–40.
    13. 13)
    14. 14)
    15. 15)
      • Khansari, M., Rabiee, H.R., Asadi, M., Ghanbari, M.: `Occlusion handling for object tracking in crowded video scenes based on the undecimated wavelet features', IEEE/ACS Int. Conf. Computer Systems and Applications, 2007, Amman, p. 692–699.
    16. 16)
      • Khansari, M., Rabiee, H.R., Asadi, M., Ghanbari, M.: `Crowded scene object tracking in presence of Gaussian white noise using undecimated wavelet features', Int. Symp. on Signal Processing and its Applications, 2007, Sharjah.
    17. 17)
      • Islam, M.M., Alam, M.S.: `Human motion tracking using mean shift clustering and discrete cosine transform', Proc. SPIE 6566, 2007, 656616.
    18. 18)
      • N.T. Binh , A. Khare . Multilevel threshold based image denoising in curvelet domain. J. Comput. Sci. Technol. , 3 , 32 - 640
    19. 19)
    20. 20)
      • A. Majumdar . Bangla basic character recognition using digital curvelet transform. J. Patt. Recogn. Res. , 1 , 17 - 26
    21. 21)
    22. 22)
    23. 23)
    24. 24)
      • Lee, Y.C., Chen, C.H.: `Face recognition based on digital curvelet transform', Int. Conf. Intelligent Systems Design and Applications, 2008, Kaohsiung, 3, p. 341–345.
    25. 25)
      • Nigam, S., Khare, A.: `Curvelet transform based object tracking', Proc. IEEE Int. Conf. on Computer and Communication Technology, 2010, Allahabad, India, p. 230–235.
    26. 26)
    27. 27)
    28. 28)
      • Porikli, F., Tuzelq, O., Meer, P.: `Covariance tracking using model update based on lie algebra', Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006, USA, p. 728–735.
    29. 29)
      • Zhang, J., Zhang, Z., Huang, W., Lu, Y., Wang, Y.: `Face recognition based on curvefaces', Third Int. Conf. on Natural Computation, 2007, p. 627–631.
    30. 30)
      • M. Sonka , V. Hlavac , R. Boyle . (1999) Image processing, analysis and machine vision.
    31. 31)
    32. 32)
    33. 33)
      • Utsumi, A., Mori, H., Ohya, J., Yachida, M.: `Multiple-human tracking using multiple cameras', Proc. IEEE Int. Conf. Automatic Face and Gesture Recognition, 1998, Nara, Japan, p. 498–503.
    34. 34)
    35. 35)
    36. 36)
      • Xiao, L., Wu, H.Z., Wei, Z.H., Bao, Y.: `Research and applications of a new computational model of human vision system based on Ridgelet transform', Proc. Int. Conf. Machine Learning and Cybernetics, 2005, Guangzhou, China, 8, p. 5170–5175.
    37. 37)
    38. 38)
    39. 39)
    40. 40)
      • G.R. Bradski , S. Clara , I. Corporation . Computer vision face tracking for use in a perceptual user interface. Int. Technol. J. , 2 , 12 - 21
    41. 41)
      • Mansouri, A., Azar, F.T., Aznaveh, A.M.: `Face tracking by 3-D dual-tree complex wavelet transform using support vector machine', Ninth Int. Symp. on Signal Processing and Its Applications, 2007, Sharjah.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2011.0023
Loading

Related content

content/journals/10.1049/iet-cvi.2011.0023
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading