Abstract
The online object tracking is a challenging problem because any useful approach must handle various nuisances including illumination changes and occlusions. Though a lot of work focus on observation models by employing sophisticated approaches for contaminated data, they commonly assume that the samples for updating observation model are uncorrupted or can be restored in updating. For instance, in particle filter based approaches every particle has to be restored for each frame, which is time-consuming and unstable. In this paper, we propose a novel scheme to decouple the observation model and its update in a particle filtering framework. Our efficient observation model is used to effectively select the most similar candidate from all particles only, by analyzing the principal component analysis (PCA) reconstruction with \(L_1\) regularization. In order to handle the contaminated samples while updating observation model, we adopt on an online robust PCA during the update of observation model. Our qualitative and quantitative evaluations on challenging dataset demonstrate that the proposed scheme is competitive to several sophisticated state of the art methods, and it is much faster.
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 subscriptionsReferences
Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77, 125–141 (2008)
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)
Mei, X., Ling, H.: Robust visual tracking using 1 minimization. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1436–1443. IEEE (2009)
Kim, S.J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large-scale l1-regularized least squares. IEEE J. Sel. Top. Sign. Process. 1, 606–617 (2007)
Mei, X., Ling, H., Wu, Y., Blasch, E., Bai, L.: Minimum error bounded efficient 1 tracker with occlusion detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1257–1264. IEEE (2011)
Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1830–1837. IEEE (2012)
Wang, N., Wang, J., Yeung, D.Y.: Online robust non-negative dictionary learning for visual tracking. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 657–664 (2013)
Wang, D., Lu, H., Yang, M.H.: Online object tracking with sparse prototypes. IEEE Trans. Image Process. 22, 314–325 (2013)
Bach, F., Jenatton, R., Mairal, J., Obozinski, G.: Optimization with sparsity-inducing penalties. Found. Trends\(\textregistered \) Mach. Learn. 4, 1–106 (2012)
Feng, J., Xu, H., Mannor, S., Yan, S.: Online PCA for contaminated data. In: Advances in Neural Information Processing Systems, pp. 764–772 (2013)
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 798–805. IEEE (2006)
Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009, CVPR 2009. pp. 983–990. IEEE (2009)
Kwon, J., Lee, K.M.: Visual tracking decomposition. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1269–1276. IEEE (2010)
Kalal, Z., Matas, J., Mikolajczyk, K.: Pn learning: Bootstrapping binary classifiers by structural constraints. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 49–56. IEEE (2010)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1409–1422 (2012)
Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1822–1829. IEEE (2012)
Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via multi-task sparse learning. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2042–2049. IEEE (2012)
Zhong, W., Lu, H., Yang, M.H.: Robust object tracking via sparsity-based collaborative model. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1838–1845. IEEE (2012)
Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and k-selection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1313–1320. IEEE (2011)
Wu, Y., Ling, H., Yu, J., Li, F., Mei, X., Cheng, E.: Blurred target tracking by blur-driven tracker. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1100–1107. IEEE (2011)
Acknowledgement
The authors would like to thank the anonymous reviewers for constructive comments that helped in improving the quality of this manuscript and Dr. NaiYan Wang for useful discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhu, H., Li, Y. (2015). Fast Inference of Contaminated Data for Real Time Object Tracking. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-16814-2_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16813-5
Online ISBN: 978-3-319-16814-2
eBook Packages: Computer ScienceComputer Science (R0)