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Scale and orientation adaptive mean shift tracking

Scale and orientation adaptive mean shift tracking

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A scale and orientation adaptive mean shift tracking (SOAMST) algorithm is proposed in this study to address the problem of how to estimate the scale and orientation changes of the target under the mean shift tracking framework. In the original mean shift tracking algorithm, the position of the target can be well estimated, whereas the scale and orientation changes cannot be adaptively estimated. Considering that the weight image derived from the target model and the candidate model can represent the possibility that a pixel belongs to the target, the authors show that the original mean shift tracking algorithm can be derived using the zeroth- and the first-order moments of the weight image. With the zeroth-order moment and the Bhattacharyya coefficient between the target model and candidate model, a simple and effective method is proposed to estimate the scale of target. Then an approach, which utilises the estimated area and the second-order centre moment, is proposed to adaptively estimate the width, height and orientation changes of the target. Extensive experiments are performed to testify the proposed method and validate its robustness to the scale and orientation changes of the target.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
      • G. Bradski . Computer vision face tracking for use in a perceptual user interface. Intel Technol. J. , 1 - 15
    5. 5)
    6. 6)
      • Comaniciu, D., Ramesh, V., Meer, P.: `Real-time tracking of non-rigid objects using mean shift', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, June 2000, Hilton Head, SC, p. 142–149, vol. 2.
    7. 7)
    8. 8)
    9. 9)
    10. 10)
      • Collins, R.: `Mean-shift blob tracking through scale space', Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2003, Wisconsin, USA, p. 234–240.
    11. 11)
      • Zivkovic, Z., Kröse, B.: `An EM-like algorithm for color-histogram-based object tracking', Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004, Washington, DC, USA, p. 798–803, vol. 1.
    12. 12)
    13. 13)
      • Srikrishnan, V., Nagaraj, T., Chaudhuri, S.: `Fragment based tracking for scale and orientation adaption', Proc. Indian Conf. on Computer Vision, Graphics & Image Processing, 2008, p. 328–335.
    14. 14)
      • Quast, K., Kaup, A.: `Scale and shape adaptive mean shift object tracking in video sequences', Proc. European Signal Processing Conf., 2009, Glasgow, Scotland, p. 1513–1517.
    15. 15)
    16. 16)
    17. 17)
      • Yang, C., Ramani, D., Davis, L.: `Efficient mean-shift tracking via a new similarity measure', Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005, San Diego, CA, p. 176–183, vol. 1.
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • R. Collins . Lecture on mean shift.
    22. 22)
      • R. Mukundan , K.R. Ramakrishnan . (1996) Moment functions in image analysis: theory and applications.
    23. 23)
    24. 24)
      • R.A. Horn , C.R. Johnson . (1991) Topics in matrix analysis.
    25. 25)
      • Zivkovic Z.: EM-shift code, http://staff.science.uva.nl/~zivkovic/PUBLICATIONS.html.
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