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http://dx.doi.org/10.5302/J.ICROS.2010.16.8.766

Effective Covariance Tracker based on Adaptive Foreground Segmentation in Tracking Window  

Lee, Jin-Wook (Korea University of Technology and Education)
Cho, Jae-Soo (Korea University of Technology and Education)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.16, no.8, 2010 , pp. 766-770 More about this Journal
Abstract
In this paper, we present an effective covariance tracking algorithm based on adaptive size changing of tracking window. Recent researches have advocated the use of a covariance matrix of object image features for tracking objects instead of the conventional histogram object models used in popular algorithms. But, according to the general covariance tracking algorithm, it can not deal with the scale changes of the moving objects. The scale of the moving object often changes in various tracking environment and the tracking window(or object kernel) has to be adapted accordingly. In addition, the covariance matrix of moving objects should be adaptively updated considering of the tracking window size. We provide a solution to this problem by segmenting the moving object from the background pixels of the tracking window. Therefore, we can improve the tracking performance of the covariance tracking method. Our several simulations prove the effectiveness of the proposed method.
Keywords
covariance tracking; covariance matrix; adaptive scale change;
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1 J. S. Cho, D. J. Kim, and D. J. Park, “Robust centroid target tracker based on novel distance features in cluttered image sequences,” IEICE Trans. Inf. and Syst. vol. E83-D, no. 12, pp. 2142-2151, Dec. 2000.
2 D. A. Montera, S. K. Rogers, D. W. Ruck, and M. E. Oxley, “Object tracking through adaptive correlation,” Optical Engineering, vol. 33, no. 1, pp. 294-302, Jan. 1994.   DOI
3 A. M. Peacock, S. Matsunaga, D. Renshaw, J. Hannah, and A. Murray, “Reference block updating when tracking with block matching algorithm,” Electronics Letters, vol. 36, no. 4, pp. 309-310, Feb. 2000.   DOI
4 K. Nickels and S. Hutchinson, “Estimation uncertainty in SSD-based feature tracking,” Image and Vision Computing, vol. 20, no. 1, pp. 47-58, 2002.   DOI
5 D. Comaniciu and P. Meer, “Kernel-based object tracking,” IEEE Trans. Pattern analysis and machine intelligence, vol. 25. no. 5, May 2003.
6 J. W. Lee and J. S. Cho, “Effective covariance tracker based on adaptive changing of tracking window,” Proc. of ICROS Annual Conference 2010, Chuncheon, Korea, pp. 505-506, May 2010.
7 F. Porikli, O. Tuzel, and P. Meer, “Covariance tracking using model update based on means on riemannian manifolds,” IEEE Conf. on Computer Vision and Pattern Recognition, 2006.
8 S. Kluckner, T. Mauthner, and H. Bischof, “A covariance approximation on euclidean space for visual tracking,” Proc. 33rd Workshop of the Austrian Association for Pattern Recognition, 2009.
9 A. Tyagi, J. W. Davis, and G. Potamianos, “Steepest descent for efficient covariance tracking,” IEEE Workshop on Motion and Video Computing, 2008.   DOI