Abstract
Robust visual object tracking is one of the key problems in computer vision. Subspace based tracking method is a promising approach in handling appearance variability. Linear Discriminant Analysis(LDA) has been applied to this problem, but LDA is not a stable algorithm especially for visual tracking. Maximum Margin Criterion(MMC) is a recently proposed discriminant criterion. Its promising specialities make it a better choice for the tracking problem. In this paper, we present a novel subspace tracking algorithm based on MMC. We also proposed an incremental version of the corresponding algorithm so that the tracker can update in realtime. Experiments show our tracking algorithm is able to track objects well under large lighting, pose and expression variation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Shen, C., Hengel, A.V.D., Brooks, M.J.: Visual Tracking via Efficient Kernel Discriminant Subspace Learning. In: Proceedings of IEEE Conference on Image Processing, vol. 2, pp. 590–593 (2005)
Ross, D., Lim, J., Yang, M.-H.: Adaptive Probabilistic Visual Tracking with Incremental Subspace Update. In: Proceedings of the Eighth European Conference on Computer Vision, vol. 2, pp. 470–482 (2004)
Li, H., Jiang, T., Zhang, K.: Efficient and Robust Feature Extraction by Maximum Margin Criterion. In: Proceedings of the Advances in Neural Information Processing Systems, vol. 16. MIT Press, Cambridge (2003)
Black, M.J., Jepson, A.D.: Eigentracking: Robust Matching and Tracking of Articulated Objects Using View-based Representation. International Journal of Computer Vision 26, 63–84 (1998)
Isard, M., Blake, A.: CONDENSATION- Conditional Density Propagation for Visual Tracking. International Journal of Computer Vision 29, 5–28 (1998)
Pang, S., Ozawa, S., Kasabov, N.: Incremental Linear Discriminant Analysis for Classification of Data Streams. IEEE Trans. on Systems, Man, and Cybernetics - Part B 35, 905–914 (2005)
Lin, R., Yang, M.-H., Levinson, S.E.: Object Tracking Using Incremental Fisher Discriminant Analysis. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 757–760 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, L., Wen, M., Wang, C., Wang, W. (2006). Robust Visual Tracking Via Incremental Maximum Margin Criterion. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_58
Download citation
DOI: https://doi.org/10.1007/11760023_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)