Abstract
In order to construct a flexible representation for robust and efficient tracking, a novel real-time tracking method based on online learning is proposed in this paper. Under Bayesian framework, RVM is used to learn the log-likelihood ratio of the statistics of the interested object region to those of the nearby backgrounds. Then, the online selected sparse vectors by RVM are integrated to construct an adaptive representation of the tracked object. Meanwhile, the trained RVM classifier is embedded into particle filtering for tracking. To avoid distraction by the particles in background region, the extreme outlier model is incorporated to describe the posterior probability distribution of all particles. Subsequently, mean-shift clustering and EM algorithm are combined to estimate the posterior state of the tracked object. Experimental results over real-world sequences have shown that the proposed method can efficiently and effectively handle drastic illumination variation, partial occlusion and rapid changes in viewpoint, pose and scale.
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
Jepson, A., Fleet, D., EI-Maraghi, T.: Robust Online Appearance Models for Visual Tracking. PAMI 10, 1296–1311 (2003)
Skocaj, D., Leonardis, A.: Weighted and robust incremental method for subspace learning. In: Proceedings of ICCV (2003)
Ross, D., Lim, J., Yang, M.-H.: Adaptive probabilistic visual tracking with incremental subspace update. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 470–482. Springer, Heidelberg (2004)
Ho, J., Lee, K.-C., Yang, M.-H., Kriegman, D.: Visual Tracking Using Learned Linear Subspace. In: Proceedings of CVPR (2004)
Matthews, I., Ishikawa, T., Baker, S.: The Template Update Problem. PAMI 6, 810–815 (2004)
Han, B., Davis, L.: On-Line Density-Based Appearance Modeling for Object Tracking. In: Proceedings of ICCV (2005)
Collins, R.T., Liu, Y.: On-line Selection of Discriminative Tracking Features. In: Proceedings of ICCV (2003)
Nguyen, H.T., Smeulders, A.W.M.: Tracking aspects of the foreground against the background. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 446–456. Springer, Heidelberg (2004)
Wang, J., Gao, W., Chen, X.: Online Selecting Discriminative Tracking Features using Particle Filter. In: Proceedings of CVPR (2005)
Avidan, S.: Ensemble Tracking. In: Proceedings of CVPR (2005)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-Based Object Tracking. PAMI 5, 564–577 (2003)
Collins, R.T., Liu, Y., Leordeanu, M.: Online Selection of Discriminative Tracking Features. PAMI 10, 1631–1643 (2005)
Tipping, M.E.: Sparse Bayesian Learning and the Relevance Vector Machine. JMLR 1, 211–244 (2001)
Williams, O., Blake, A., Cipolla, R.: Sparse Bayesian Learning for Efficient Visual Tracking. PAMI 8, 1–13 (2005)
Avidan, S.: Support Vector Tracking. PAMI 8, 1064–1072 (2004)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A Tutorial on Particle Filters for Online Nonliner/Non-Gaussian Bayesian Tracking. Signal Processing 2, 174–188 (2002)
Philomin, V., Duraiswami, R., Davis, L.: Quasi-random sampling for condensation. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 134–149. Springer, Heidelberg (2000)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 5, 603–619 (2002)
Zivkovic, Z., Krose, B.: An EM-like Algorithm for Color-Histogram-Based Object Tracking. In: Proceedings of CVPR (2004)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. IJCV 2, 91–110 (2004)
Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. PAMI 10, 1615–1630 (2005)
Viola, P., Jones, M.J.: Robust Real-Time Face Detection. IJCV 2, 137–154 (2004)
http://www.cs.toronto.edu/vis/projects/dudekfaceSequence.html
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
Lei, Y., Ding, X., Wang, S. (2006). Adaptive Sparse Vector Tracking Via Online Bayesian Learning. In: Zheng, N., Jiang, X., Lan, X. (eds) Advances in Machine Vision, Image Processing, and Pattern Analysis. IWICPAS 2006. Lecture Notes in Computer Science, vol 4153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11821045_4
Download citation
DOI: https://doi.org/10.1007/11821045_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37597-5
Online ISBN: 978-3-540-37598-2
eBook Packages: Computer ScienceComputer Science (R0)