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Paper Count: 32119
A Robust Visual Tracking Algorithm with Low-Rank Region Covariance
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070433Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1437
 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, 2008.
 D. A. Ross, et al., "Incremental learning for robust visual tracking," International Journal of Computer Vision, vol. 77, pp. 125-141, May 2008.
 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 University, 1999.
 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, pp.1-25, 1996.
 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.