Incremental Learning for Robust Visual Tracking

Springer Science and Business Media LLC - Tập 77 Số 1-3 - Trang 125-141 - 2008
David A. Ross1, Jongwoo Lim2, Ruei-Sung Lin3, Ming–Hsuan Yang2
1University of Toronto, 10 Kings College Road, Toronto, ON, M55 3G4, Canada
2Honda Research Institute, 800 California Street, Mountain View, CA, 94041, USA
3Motorola Labs, 1303 E Algonquin Rd., Schaumburg, IL, 60196, USA

Tóm tắt

Từ khóa


Tài liệu tham khảo

Adelson, E. H., & Bergen, J. R. (1991). The plenoptic function and the elements of early vision. In M. Landy & J. A. Movshon (Eds.), Computational models of visual processing (pp. 1–20). Cambridge: MIT Press.

Avidan, S. (2001). Support vector tracking. In Proceedings of IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 184–191).

Belhumeur, P., & Kreigman, D. (1997). What is the set of images of an object under all possible lighting conditions. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 270–277).

Birchfield, S. (1998). Elliptical head tracking using intensity gradient and color histograms. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 232–237).

Black, M. J., & Jepson, A. D. (1996). Eigentracking: robust matching and tracking of articulated objects using view-based representation. In B. Buxton & R. Cipolla (Eds.), LNCS : Vol. 1064. Proceedings of the fourth European conference on computer vision (pp. 329–342). Berlin: Springer.

Black, M. J., Fleet, D. J., & Yacoob, Y. (1998). A framework for modeling appearance change in image sequence. In Proceedings of IEEE international conference on computer vision (pp. 660–667).

Brand, M. (2002). Incremental singular value decomposition of uncertain data with missing values. In A. Heyden, G. Sparr, M. Nielsen & P. Johansen (Eds.), LNCS : Vol. 2350. Proceedings of the seventh European conference on computer vision (pp. 707–720). Berlin: Springer.

Comaniciu, D., Ramesh, V., & Meer, P. (2003). Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5), 564–577.

Cootes, T., Edwards, G., & Taylor, C. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 681–685.

Georgescu, B., Comaniciu, D., Han, T. X., & Zhou, X. S. (2004). Multi-model component-based tracking using robust information fusion. In 2nd workshop on statistical methods in video processing, May 2004

Golub, G. H., & Van Loan, C. F. (1996) Matrix computations. The Johns Hopkins University Press.

Hager, G., & Belhumeur, P. (1996) Real-time tracking of image regions with changes in geometry and illumination. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 403–410).

Hall, P., Marshall, D., & Martin, R. (1998). Incremental eigenanalysis for classification. In Proceedings of British machine vision conference (pp. 286–295).

Hall, P., Marshall, D., & Martin, R. (2002). Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition. Image and Vision Computing, 20(13–14), 1009–1016.

Harville, M. (2002). A framework for high-level feedback to adaptive, per-pixel mixture of Gaussian background models. In A. Heyden, G. Sparr, M. Nielsen & P. Johansen (Eds.), LNCS : Vol. 2352. Proceedings of the seventh European conference on computer vision (pp. 531–542). Berlin: Springer.

Isard, M., & Blake, A. (1996). Contour tracking by stochastic propagation of conditional density. In B. Buxton & R. Cipolla (Eds.), LNCS : Vol. 1064. Proceedings of the fourth European conference on computer vision (pp. 343–356). Berlin: Springer.

Jepson, A. D., Fleet, D. J., & El-Maraghi, T. F. (2003). Robust online appearance models for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), 1296–1311.

Jolliffe, I. T. (2002). Principal component analysis. Berlin: Springer.

La Cascia, M., & Sclaroff, S. (1999). Fast, reliable head tracking under varying illumination. In Proceedings of IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 604–608).

Levy, A., & Lindenbaum, M. (2000). Sequential Karhunen–Loeve basis extraction and its application to images. IEEE Transactions on Image Processing, 9(8), 1371–1374.

Lim, J., Ross, D., Lin, R.-S., & Yang, M.-H. (2005). Incremental learning for visual tracking. In L. Saul, Y. Weiss & L. Bottou (Eds.), Advances in neural information processing systems (pp. 793–800). Cambridge: MIT Press.

Lin, R.-S., Ross, D., Lim, J., & Yang, M.-H. (2005). Adaptive discriminative generative model and its applications. In L. Saul, Y. Weiss & L. Bottou (Eds.), Advances in neural information processing systems (pp. 801–808). Cambridge: MIT Press.

Lucas, B., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In Proceedings of international joint conference on artificial intelligence (pp. 674–679).

Matthews, I., Ishikawa, T., & Baker, S. (2004). The template update problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6), 810–815.

Moghaddam, B., & Pentland, A. (1995). Probabilistic visual learning for object detection. In Proceedings of IEEE international conference on computer vision (pp. 786–793).

Murase, H., & Nayar, S. (1995). Visual learning and recognition of 3d objects from appearance. International Journal of Computer Vision, 14(1), 5–24.

North, B., & Blake, A. (1998). Learning dynamical models using expectation-maximization. In Proceedings of IEEE international conference on computer vision (pp. 384–389).

Rasmussen, C., & Hager, G. (1998). Joint probabilistic techniques for tracking multi-part objects. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 16–21).

Ross, D., Lim, J., & Yang, M.-H. (2004). Adaptive probabilistic visual tracking with incremental subspace update. In A. Heyden, G. Sparr, M. Nielsen & P. Johansen (Eds.), LNCS : Vol. 2350. Proceedings of the eighth European conference on computer vision (pp. 707–720). Berlin: Springer.

Roweis, S. (1997). EM algorithms for PCA and SPCA. In M. I. Jordan, M. J. Kearns & S. A. Solla (Eds.), Advances in neural information processing systems 10 (pp. 626–632). Cambridge: MIT Press.

Tipping, M. E., & Bishop, C. M. (1999). Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B, 61(3), 611–622.

Toyama, K., & Blake, A. (2001). Probabilistic tracking in metric space. In Proceedings of IEEE international conference on computer vision (pp. 50–57).

Vermaak, J., Lawrence, N., & Perez, P. (2003). Variational inference for visual tracking. In Proceedings of IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 773–780).

Williams, O., Blake, A., & Cipolla, R. (2003). A sparse probabilistic learning algorithms for real-time tracking. In Proceedings of IEEE international conference on computer vision (Vol. 1, pp. 353–360).