Robust mean-shift tracking with corrected background-weighted histogram

Robust mean-shift tracking with corrected background-weighted histogram

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

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

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
Your details
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 background-weighted histogram (BWH) algorithm proposed by Comaniciu et al. attempts to reduce the interference of background in target localisation in mean-shift tracking. However, the authors prove that the weights assigned to pixels in the target candidate region by BWH are proportional to those without background information, that is, BWH does not introduce any new information because the mean-shift iteration formula is invariant to the scale transformation of weights. Then a corrected BWH (CBWH) formula is proposed by transforming only the target model but not the target candidate model. The CBWH scheme can effectively reduce background's interference in target localisation. The experimental results show that CBWH can lead to faster convergence and more accurate localisation than the usual target representation in mean-shift tracking. Even if the target is not well initialised, the proposed algorithm can still robustly track the object, which is hard to achieve by the conventional target representation.


    1. 1)
    2. 2)
      • Comaniciu, D., Ramesh, V., Meer, P.: `Real-time tracking of non-rigid objects using mean shift', Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2000, Hilton Head, SC, USA, p. 142–149.
    3. 3)
    4. 4)
      • G. Bradski . Computer vision face tracking for use in a perceptual user interface. Intel Technol. J. , 12 - 21
    5. 5)
    6. 6)
    7. 7)
    8. 8)
      • Wang, J., Thiesson, B., Xu, Y., Cohen, M.F.: `Image and video segmentation by anisotropic kernel mean shift', Proc. European Conf. Computer Vision, May 2004, Prague, Czech Republic, 3022, p. 238–249.
    9. 9)
      • Paris, S., Durand, F.: `A topological approach to hierarchical segmentation using mean shift', Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2007, Minnesota, USA, p. 1–8.
    10. 10)
      • Luo, Q., Khoshgoftaar, T.M.: `Efficient image segmentation by mean shift clustering and MDL-guided region merging', IEEE Proc. Int. Conf. Tools with Artificial Intelligence, November 2004, Florida, USA, p. 337–343.
    11. 11)
    12. 12)
      • Collins, R.: `Mean-shift blob tracking through scale space', Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2003, Wisconsin, USA, p. 234–240.
    13. 13)
      • Zivkovic, Z., Kröse, B.: `An EM-like algorithm for color-histogram-based object tracking', Proc. IEEE Conf. Computer Vision and Pattern Recognition, July 2004, Washington, DC, USA, I, p. 798–803.
    14. 14)
      • Yang, C., Ramani, D., Davis, L.: `Efficient mean-shift tracking via a new similarity measure', Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2005, San Diego, CA, I, p. 176–183.
    15. 15)
      • Yilmaz, A.: `Object tracking by asymmetric kernel mean shift with automatic scale and orientation selection', Proc. IEEE Conf. Computer Vision and pattern Recognition, June 2007, Minnesota, USA, I, p. 1–6.
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • L. Li , Z. Feng . An efficient object tracking method based on adaptive nonparametric approach. Opto-Electron. Rev. , 4 , 325 - 330
    22. 22)
      • Allen, J., Xu, R., Jin, J.: `Mean shift object tracking for a SIMD computer', Proc. Int. Conf. Information Technology and Applications, July 2005, Sydney, Australia, I, p. 692–697.

Related content

This is a required field
Please enter a valid email address