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Mathematical Problems in Engineering
Volume 2014, Article ID 787093, 12 pages
http://dx.doi.org/10.1155/2014/787093
Research Article

Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

1College of Science, Harbin Engineering University, Harbin 150001, China
2College of Computer Science and Technology, Harbin Engineering University, Harbin 151001, China
3Science and Technology on Underwater Acoustic Antagonizing Laboratory, Systems Engineering Research Institute of CSSC, Beijing 100036, China

Received 25 June 2014; Accepted 25 August 2014; Published 9 November 2014

Academic Editor: Guoqiang Zhang

Copyright © 2014 Ting-quan Deng et al. 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. H. Yang, L. Shao, F. Zheng, L. Wang, and Z. Song, “Recent advances and trends in visual tracking: a review,” Neurocomputing, vol. 74, no. 18, pp. 3823–3831, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. M. J. Black and A. D. Jepson, “Eigentracking: robust matching and tracking of articulated objects using a view-based representation,” International Journal of Computer Vision, vol. 26, no. 1, pp. 63–84, 1998. View at Publisher · View at Google Scholar · View at Scopus
  3. I. Matthews, T. Ishikawa, and S. Baker, “The template update problem,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 810–815, 2004. 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. 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
  6. 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
  7. 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), pp. 798–805, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. 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
  9. 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, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. 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
  11. H. Grabner, M. Grabner, and H. Bischof, “Real-time tracking via on-line boosting,” in Proceedings of the British Machine Vision Conference (BMVC '06), pp. 47–56, September 2006. View at Scopus
  12. H. Grabner, C. Leistner, and H. Bischof, “Semi-supervised on-line boosting for robust tracking,” in Proceedings of the European Conference on Computer Vision, pp. 234–247, Marseille, France, October 2008.
  13. S. Avidan, “Ensemble tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 2, pp. 261–271, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. B. Babenko, M.-H. Yang, and S. Belongie, “Robust object tracking with online multiple instance learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1619–1632, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. K. Zhang and H. Song, “Real-time visual tracking via online weighted multiple instance learning,” Pattern Recognition, vol. 46, no. 1, pp. 397–411, 2013. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  16. 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
  17. R. T. Collins, Y. Liu, and M. Leordeanu, “Online selection of discriminative tracking features,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1631–1643, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Kwon and K. M. Lee, “Visual tracking decomposition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 1269–1276, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-learning-detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 7, pp. 1409–1422, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. K. Zhang, L. Zhang, and M. H. Yang, “Real-time compressive tracking,” in Proceedings of the European Conference on Computer Vision, pp. 864–877, 2012.
  21. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, “A survey of multilinear subspace learning for tensor data,” Pattern Recognition, vol. 44, no. 7, pp. 1540–1551, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  22. W. Yang, C. Sun, and L. Zhang, “A multi-manifold discriminant analysis method for image feature extraction,” Pattern Recognition, vol. 44, no. 8, pp. 1649–1657, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  23. J. Sherrah, B. Ristic, and N. J. Redding, “Particle filter to track multiple people for visual surveillance,” IET Computer Vision, vol. 5, no. 4, pp. 192–200, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. P. Dollár, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: an evaluation of the state of the art,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 743–761, 2012. View at Publisher · View at Google Scholar · View at Scopus