Region covariance (RC) descriptor is an effective
and efficient feature for visual tracking. Current RC-based tracking
algorithms use the whole RC matrix to track the target in video
directly. However, there exist some issues for these whole RCbased
algorithms. If some features are contaminated, the whole RC
will become unreliable, which results in lost object-tracking. In
addition, if some features are very discriminative to the
background, other features are still processed and thus reduce the
efficiency. In this paper a new robust tracking method is proposed,
in which the whole RC matrix is decomposed into several low rank
matrices. Those matrices are dynamically chosen and processed so
as to achieve a good tradeoff between discriminability and
complexity. Experimental results have shown that our method is
more robust to complex environment changes, especially either
when occlusion happens or when the background is similar to the
target compared to other RC-based methods.
 Oncel Tuzel, et al, "Region covariance: A fast descriptor for detection
and classification", Computer Vision - ECCV 2006, Pt 2,
Proceedings, vol. 3952, pp. 589-600, 2006.
 Fatih Porikli, et al., "Covariance Tracking using Model Update Based
on Means on Riemannian Manifold", 2006 IEEE Conf. on Computer
Vision and Pattern Recognition, 2006.
 Y. Wu, et al., "Probabilistic Tracking on Riemannian Manifolds," 19th
International Conf. on Pattern Recognition, Vols 1-6, pp. 229-232,
 D. A. Ross, et al., "Incremental learning for robust visual tracking,"
International Journal of Computer Vision, vol. 77, pp. 125-141, May
 X. Li, et al., "Visual tracking via incremental Log-Euclidean
Riemannian subspace learning," 2008 IEEE Conf. on Computer
Vision and Pattern Recognition, Vols 1-12, pp. 1349-1356, 2008.
 V. Arsigny, et al., "Log-Euclidean metrics for fast and simple calculus
on diffusion tensors", Magnetic Resonance in Medicine, vol. 56, pp.
411-421, Aug 2006.
 Y. Wu, J. Cheng, J. Wang, H. Lu, "Real-time visual tracking via
incremental covariance tensor learning", International Conf. on
Computer Vision, 2009.
 F┬¿orstner, W. Moonen, B. "A metric for covariance matrices"
Technical report Dept. of Geodesy and Geoinformatics, Stuttgart
 X. Pennec, et al., "A Riemannian Framework for Tensor Computing",
International Journal of Computer Vision, vol. 66, pp. 41-66, 2006.
 G. Kitagawa, "Monte Carlo filter and smoother for non-Gaussian nonlinear
state space models", J. Comput, Graph Statist, vol. 5, no. 1,
 A. Daucet, S. Godsill, and C. Andrieu, "on sequential Monte Carlo
sampling method for Bayesian filtering", Statistics and Computing,
vol. 10, pp. 197-208, 2000.