nips nips2004 nips2004-83 nips2004-83-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jongwoo Lim, David A. Ross, Ruei-sung Lin, Ming-Hsuan Yang
Abstract: Most existing tracking algorithms construct a representation of a target object prior to the tracking task starts, and utilize invariant features to handle appearance variation of the target caused by lighting, pose, and view angle change. In this paper, we present an efficient and effective online algorithm that incrementally learns and adapts a low dimensional eigenspace representation to reflect appearance changes of the target, thereby facilitating the tracking task. Furthermore, our incremental method correctly updates the sample mean and the eigenbasis, whereas existing incremental subspace update methods ignore the fact the sample mean varies over time. The tracking problem is formulated as a state inference problem within a Markov Chain Monte Carlo framework and a particle filter is incorporated for propagating sample distributions over time. Numerous experiments demonstrate the effectiveness of the proposed tracking algorithm in indoor and outdoor environments where the target objects undergo large pose and lighting changes. 1
[1] E. H. Adelson and J. R. Bergen. The plenoptic function and the elements of early vision. In M. Landy and J. A. Movshon, editors, Computational Models of Visual Processing, pp. 1–20. MIT Press, 1991.
[2] P. Belhumeur and D. Kreigman. 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, 1997.
[3] M. J. Black, D. J. Fleet, and Y. Yacoob. A framework for modeling appearance change in image sequence. In Proceedings of the Sixth IEEE International Conference on Computer Vision, pp. 660–667, 1998.
[4] M. J. Black and A. D. Jepson. Eigentracking: Robust matching and tracking of articulated objects using view-based representation. In Proceedings of European Conference on Computer Vision, pp. 329–342, 1996.
[5] M. Brand. Incremental singular value decomposition of uncertain data with missing values. In Proceedings of the Seventh European Conference on Computer Vision, volume 4, pp. 707–720, 2002.
[6] G. H. Golub and C. F. Van Loan. Matrix Computations. The Johns Hopkins University Press, 1996.
[7] G. Hager and P. Belhumeur. 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, 1996.
[8] P. Hall, D. Marshall, and R. Martin. Incremental eigenanalysis for classification. In Proceedings of British Machine Vision Conference, pp. 286–295, 1998.
[9] M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density. In Proceedings of the Fourth European Conference on Computer Vision, volume 2, pp. 343–356, 1996.
[10] A. D. Jepson, D. J. Fleet, and T. F. El-Maraghi. Robust online appearance models for visual tracking. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pp. 415–422, 2001.
[11] A. Levy and M. Lindenbaum. Sequential Karhunen-Loeve basis extraction and its application to images. IEEE Transactions on Image Processing, 9(8):1371–1374, 2000.
[12] B. Moghaddam and A. Pentland. Probabilistic visual learning for object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):696–710, 1997.
[13] H. Murase and S. Nayar. Visual learning and recognition of 3d objects from appearance. International Journal of Computer Vision, 14(1):5–24, 1995.
[14] D. Ross, J. Lim, and M.-H. Yang. Adaptive probabilistic visual tracking with incremental subspace update. In Proceedings of the Eighth European Conference on Computer Vision, volume 2, pp. 470–482, 2004.
[15] S. Roweis. EM algorithms for PCA and SPCA. In Advances in Neural Information Processing Systems 10, pp. 626–632, 1997.
[16] M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B, 61(3):611–622, 1999.