Abstract
Object tracking by image registration based on the Lucas-Kanade method has been studied over decades. The classical method is known to be sensitive to illumination changes, pose variation and occlusion. A great number of papers have been presented to address this problem. Despite great advances achieved thus far, robust registration-based tracking in challenging conditions remains unsolved. This paper presents a novel method which extends the Lucas-Kanade using the sparse representation. Our objective function involves joint optimization of the warp function and the optimal linear combination of the test image with a set of basis vectors in a dictionary. The objective function is regularized by ℓ1 norm of the linear combination coefficients. It is a non-linear and non-convex problem and we minimize it by alternating between the warp function and coefficients. We thus achieve an efficient algorithm which iteratively solves the LASSO and classical Lucas-Kanade by optimizing one while keeping another fixed. Unlike existing sparsity-based work that uses exemplar templates as the object model, we explore the low-dimensional linear subspace of the object appearances for object representation. For adaptation to dynamical scenarios, the mean vector and basis vectors of the appearance subspace are updated online by incremental SVD. Experiments demonstrate the promising performance of the proposed method in challenging image sequences.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. of the 7th Int. Joint Conference on Artificial Intelligence, IJCAI 1981, pp. 674–679 (1981)
Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vision 56, 221–255 (2004)
Candès, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics 59, 1207–1223 (2006)
Tibshirani, R.: Regression shrinkage and selection via the lasso: a retrospective. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73, 273–282 (2011)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)
Matthews, L., Ishikawa, T., Baker, S.: The template update problem. IEEE Trans. Pattern Anal. Mach. Intell. 26, 810–815 (2004)
Hager, G.D., Belhumeur, P.N.: Efficient region tracking with parametric models of geometry and illumination. IEEE Trans. Pattern Anal. Mach. Intell. 20, 1025–1039 (1998)
Shi, J., Tomasi, C.: Good features to track. In: Int. Conf. on Computer Vision and Pattern Recognition, pp. 593–600 (1994)
Birchfield, S., Pundlik, S.: Joint tracking of features and edges. In: Int. Conf. on Computer Vision and Pattern Recognition, pp. 1–6 (2008)
Kim, S.J., Frahm, J.M., Pollefeys, M.: Joint feature tracking and radiometric calibration from auto-exposure video. In: Int. Conf. on Computer Vision, pp. 1–8 (2007)
Bayro-Corrochano, E., Orteg-Aguilar, J.: Lie algebra approach for tracking and 3d motion estimation using monocular vision. Image and Vision Computing 25, 907–921 (2007)
Mei, X., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2259–2272 (2011)
Jia, X., Lu, H., Yang, M.: Tracking via adaptive structural local sparse appearance model. In: Int. Conf. on Computer Vision and Pattern Recognition (2012)
Zhong, W., Lu, H., Yang, M.: Robust object tracking via sparsity-based collaborative model. In: Int. Conf. on Computer Vision and Pattern Recognition (2012)
Wu, Y., Shen, B., Ling, H.: Online robust image alignment via iterative convex optimization. In: Int. Conf. on Computer Vision and Pattern Recognition, pp. 1808–1814 (2012)
Huang, J., Huang, X., Metaxas, D.: Simultaneous image transformation and sparse representation recovery. In: Int. Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., Ma, Y.: Toward a practical face recognition system: Robust alignment and illumination by sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 34, 372–386 (2012)
Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vision 77, 125–141 (2008)
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: Proc. of the 26th Annual Int. Conf. on Machine Learning, ICML 2009, pp. 689–696. ACM, New York (2009)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25, 564–577 (2003)
Leichter, I., Lindenbaum, M., Rivlin, E.: Tracking by affine kernel transformations using color and boundary cues. IEEE Trans. on Pattern Anal. Mach. Intell. 31, 164–171 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, P., Wang, Q. (2013). Robust Registration-Based Tracking by Sparse Representation with Model Update. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-37431-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37430-2
Online ISBN: 978-3-642-37431-9
eBook Packages: Computer ScienceComputer Science (R0)