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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 147353, 12 pages
http://dx.doi.org/10.1155/2014/147353
Research Article

Low-Rank Representation-Based Object Tracking Using Multitask Feature Learning with Joint Sparsity

Department of Image, Chung-Ang University, Seoul 156-756, Republic of Korea

Received 13 August 2014; Revised 7 November 2014; Accepted 7 November 2014; Published 23 November 2014

Academic Editor: Sergei V. Pereverzyev

Copyright © 2014 Hyuncheol Kim and Joonki Paik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Wang, H. Lu, F. Yang, and M.-H. Yang, “Superpixel tracking,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '11), pp. 1323–1330, IEEE, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. X. Jia, H. Lu, and M.-H. Yang, “Visual tracking via adaptive structural local sparse appearance model,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 1822–1829, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564–577, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, “Incremental learning for robust visual tracking,” International Journal of Computer Vision, vol. 77, no. 1–3, pp. 125–141, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Adam, E. Rivlin, and I. Shimshoni, “Robust fragments-based tracking using the integral histogram,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '06), vol. 1, pp. 798–805, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. W. Hu, X. Li, X. Zhang, X. Shi, S. Maybank, and Z. Zhang, “Incremental tensor subspace learning and its applications to foreground segmentation and tracking,” International Journal of Computer Vision, vol. 91, no. 3, pp. 303–327, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Grabner, C. Leistner, and H. Bischof, “Semi-supervised on-line boosting for robust tracking,” in Computer Vision—ECCV, pp. 234–247, Springer, 2008. View at Google Scholar
  8. S. Avidan, “Support Vector Tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp. 1064–1072, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Mei and H. Ling, “Robust visual tracking and vehicle classification via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 11, pp. 2259–2272, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Mei, H. Ling, Y. Wu, E. Blasch, and L. Bai, “Minimum error bounded efficient 1 tracker with occlusion detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 1257–1264, Providence, RI, USA, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. B. Liu, L. Yang, J. Huang, P. Meer, L. Gong, and C. Kulikowski, “Robust and fast collaborative tracking with two stage sparse optimization,” in Proceedings of the 11th European Conference on Computer Vision (ECCV '10), pp. 624–637, Springer, 2010.
  12. B. Liu, J. Huang, C. Kulikowski, and L. Yang, “Robust visual tracking using local sparse appearance model and k-selection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 12, pp. 2968–2981, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. H. Kim and J. Paik, “Object tracking using compressive local appearance model with l1-regularisation,” Electronics Letters, vol. 50, no. 6, pp. 444–446, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Bao, Y. Wu, H. Ling, and H. Ji, “Real time robust L1 tracker using accelerated proximal gradient approach,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 1830–1837, IEEE, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, “Robust visual tracking via structured multi-task sparse learning,” International Journal of Computer Vision, vol. 101, no. 2, pp. 367–383, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. Y. Bai and M. Tang, “Object tracking via robust multitask sparse representation,” IEEE Signal Processing Letters, vol. 21, no. 8, pp. 909–913, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, “Low-rank sparse learning for robust visual tracking,” in Computer Vision—ECCV 2012, pp. 470–484, Springer, 2012. View at Google Scholar
  18. C. Zhang, R. Liu, T. Qiu, and Z. Su, “Robust visual tracking via incremental low-rank features learning,” Neurocomputing, vol. 131, pp. 237–247, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Zhang, S. Liu, N. Ahuja, M.-H. Yang, and B. Ghanem, “Robust visual tracking via consistent low-rank sparse learning,” International Journal of Computer Vision, pp. 1–20, 2014. View at Publisher · View at Google Scholar
  20. E. J. Candès, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?” Journal of the ACM, vol. 58, no. 3, article 11, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  21. D. Wang, H. Lu, and M.-H. Yang, “Online object tracking with sparse prototypes,” IEEE Transactions on Image Processing, vol. 22, no. 1, pp. 314–325, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. A. Argyriou, T. Evgeniou, and M. Pontil, “Multi-task feature learning,” in Advances in Neural Information Processing Systems, vol. 19, pp. 41–48, MIT Press, 2007. View at Google Scholar
  23. A. Argyriou, T. Evgeniou, and M. Pontil, “Convex multi-task feature learning,” Machine Learning, vol. 73, no. 3, pp. 243–272, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. Z. Lin, M. Chen, and Y. Ma, “The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices,” In press, http://arxiv.org/abs/1009.5055.
  25. Y. Xu, W. Yin, Z. Wen, and Y. Zhang, “An alternating direction algorithm for matrix completion with nonnegative factors,” Frontiers of Mathematics in China, vol. 7, no. 2, pp. 365–384, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  26. Z. Lin, R. Liu, and Z. Su, “Linearized alternating direction method with adaptive penalty for low-rank representation,” in Advances in Neural Information Processing Systems, pp. 612–620, 2011. View at Google Scholar
  27. J.-F. Cai, E. J. Candès, and Z. Shen, “A singular value thresholding algorithm for matrix completion,” SIAM Journal on Optimization, vol. 20, no. 4, pp. 1956–1982, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. K. Bredies and D. A. Lorenz, “Linear convergence of iterative soft-thresholding,” Journal of Fourier Analysis and Applications, vol. 14, no. 5-6, pp. 813–837, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  29. T. Wang, I. Y. H. Gu, and P. Shi, “Object tracking using incremental 2D-PCA learning and ML estimation,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '07), vol. 1, pp. I-933–I-936, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. M. Everingham, L. van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (VOC) challenge,” International Journal of Computer Vision, vol. 88, no. 2, pp. 303–338, 2010. View at Publisher · View at Google Scholar · View at Scopus