Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Liwicki, Stephan; Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Pantic, Maja (2011)
Languages: English
Types: Unknown
We introduce a fast and robust subspace-based approach to appearance-based object tracking. The core of our approach is based on Fast Robust Correlation (FRC), a recently proposed technique for the robust estimation of large translational displacements. We show how the basic principles of FRC can be naturally extended to formulate a robust version of Principal Component Analysis (PCA) which can be efficiently implemented incrementally and therefore is particularly suitable for robust real-time appearance-based object tracking. Our experimental results demonstrate that the proposed approach outperforms other state-of-the-art holistic appearance-based trackers on several popular video sequences.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] M. Black and A. Jepson, “Eigentracking: Robust matching and tracking of articulated objects using a view-based representation,” in IJCV'98, vol. 26, 1998, pp. 63 - 84.
    • [2] A. Jepson, D. Fleet, and T. El-Maraghi, “Robust Online Appearance Models for Visual Tracking,” in IEEE Trans. Pattern Anal. Mach. Intell., 2003, pp. 1296 - 1311.
    • [3] S. Zhou, R. Chellappa, and B. Moghaddam, “Visual Tracking and Recognition Using Appearance-Adaptive Models in Particle Filters,” in IEEE Trans. Image Process., vol. 13, 2004, pp. 1491 - 1506.
    • [4] S. Avidan, “Support vector tracking,” in IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, 2004, pp. 1064 - 1072.
    • [5] B. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in Int. joint Conf. on A. I., vol. 3, 1981, pp. 674 - 679.
    • [6] G. Hager and P. Belhumeur, “Efficient Region Tracking with Parametric Models of Geometry and Illumination,” in IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, 1998, p. 1025.
    • [7] S. Baker and I. Matthews, “Equivalence and Efficiency of Image Alignment Algorithms,” in CVPR'01, vol. 1, 2001, pp. 1090 - 1097.
    • [8] I. Matthews and S. Baker, “Active Appearance Models Revisited,” in IJCV'04, vol. 60, 2004, pp. 135 - 164.
    • [9] D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, “Incremental Learning for Robust Visual Tracking,” in IJCV'08, vol. 77, 2008, pp. 125 - 141.
    • [10] T.-J. Chin and D. Suter, “Incremental Kernel Principal Component Analysis,” in IEEE Trans. Image Process., vol. 16, 2007, pp. 1662 - 1674.
    • [11] M. Isard and A. Blake, “Contour Tracking by Stochastic Propagation of Conditional Density,” in ECCV'96, 1996, pp. 343 - 356.
    • [12] F. de la Torre and M. Black, “A Framework for Robust Subspace Learning,” in IJCV'03, vol. 54, 2003, pp. 117 - 142.
    • [13] A. Fitch, A. Kadyrov, W. Christmas, and J. Kittler, “Fast Robust Correlation,” in IEEE Trans. Image Process., vol. 14, 2005, pp. 1063 - 1073.
    • [14] M. Turk and A. Pentland, “Eigenfaces for Recognition,” in Journal of Cognitive Neuroscience, vol. 3, 1991, pp. 71 - 86.
    • [15] B. Scho¨lkopf, A. Smola, and K.-R. Mu¨ller, “Nonlinear Component Analysis as a Kernel Eigenvalue Problem,” in Neural computation, vol. 10, 1998, pp. 1299 - 1319.
    • [16] M. Tipping and C. Bishop, “Probabilistic Principal Component Analysis,” in Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 61, 1999, pp. 611 - 622.
  • No related research data.
  • No similar publications.

Share - Bookmark

Funded by projects


Cite this article