Abstract
In this paper, we introduce a novel incremental subspace based object tracking algorithm. The two major contributions of our work are the Robust PCA based occlusion handling scheme and revised incremental PCA algorithm. The occlusion handling scheme fully makes use of the merits of Robust PCA and achieves promising results in occlusion, clutter, noisy and other complex situations for the object tracking task. Besides, the introduction of incremental PCA facilitates the subspace updating process and possesses several benefits compared with traditional R-SVD based updating methods. The experiments show that our proposed algorithm is efficient and effective to cope with common object tracking tasks, especially with strong robustness due to the introduction of Robust PCA.
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References
Isard, M., Blake, A.: Condensation: conditional density propagation for visual tracking. International Journal of Computer Vision 1, 5–28 (1998)
Avidan, S.: Ensemble tracking. In: Conference on Computer Vison and Pattern Recognition, vol. 2, pp. 494–501 (2005)
Black, M.J., Jepson, A.D.: Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. International Journal of Computer Vision 26, 63–84 (1998)
Lim, J., Ross, D., Lin, R.S., Yang, M.H.: Incremental learning for visual tracking. Advances in Neural Information Processing Systems 1, 793–800 (2004)
Leonardis, A., Bischof, H.: Robust recognition using eigenimages. Computer Vision and Image Understanding 78, 99–118 (2000)
Skocaj, D., Leonardis, A.: Weighted and robust incremental method for subspace learning. In: International Conference on Computer Vision, vol. 2, pp. 1494–1501 (2003)
Levy, A., Lindenbaum, M.: Sequential Karhunen-Loeve Basis Extraction and its Application to Images. IEEE Transactions on Image processing 9, 1371–1374 (2000)
Ross, D.A., Lim, J., Lin, R.-s., Yang, M.-h., Lim, J., Yang, M.-h.: Incremental Learning for Robust Visual Tracking. International Journal of Computer Vision 77, 125–141 (2008)
Tipping, M.E., Bishop, C.M.: Probabilistic Principal Component Analysis. Journal of the Royal Statistical Society 61, 611–622 (1999)
Ho, J., Lee, K.C., Yang, M.H., Kriegman, D.: Visual Tracking Using Learned Linear Subspaces. In: Computer Vision and Pattern Recognition, pp. 782–789 (2004)
Zhang, X., Hu, W., Maybank, S., Li, X.: Graph Based Discriminative Learning for Robust and Efficient Object Tracking. In: International Conference on Computer Vision (2007)
Li, X., Hu, W., Zhang, Z., Zhang, X., Luo, G.: Robust Visual Tracking Based on Incremental Tensor Subspace Learning. In: International Conference on Computer Vision (2007)
Li, X., Hu, W., Zhang, Z., Zhang, X., Zhu, M., Cheng, J.: Visual Tracking Via Incremental Log-Euclidean Riemannian Subspace Learning. In: CVPR (2008)
Khan, Z., Balch, T., Dellaert, F.: A rao-blackwellized particle filter for eigentracking. In: CVPR, pp. 980–986 (2004)
Lin, R.-s., Ross, D., Lim, J., Yang, M.-h.: Adaptive discriminative generative model and its applications. In: Advances in Neural Information Processing Systems, pp. 801–808 (2004)
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Yu, G., Hu, Z., Lu, H. (2009). Robust Incremental Subspace Learning for Object Tracking. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_93
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DOI: https://doi.org/10.1007/978-3-642-10677-4_93
Publisher Name: Springer, Berlin, Heidelberg
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