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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 352634, 7 pages
http://dx.doi.org/10.1155/2013/352634
Research Article

Robust Online Object Tracking Based on Feature Grouping and 2DPCA

College of Information & Communication Engineering, Dalian Nationalities University, Dalian 116600, China

Received 20 March 2013; Revised 7 May 2013; Accepted 13 May 2013

Academic Editor: Shangbo Zhou

Copyright © 2013 Ming-Xin Jiang 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. A. Yilmaz, O. Javed, and M. Shah, “Object tracking: a survey,” ACM Computing Surveys, vol. 38, no. 4, pp. 229–240, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. M. X. Jiang, Z. J. Shao, and H. Y. Wang, “Real-time object tracking algorithm with cameras mounted on moving platforms,” International Journal of Image and Graphics, vol. 12, Article ID 1250020, 12 pages, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  3. M. X. Kristan, S. Kovačič, A. Leonardis, and J. Perš, “A two-stage dynamic model for visual tracking,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 40, no. 6, pp. 1505–1520, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. M. X. Jiang, M. Li, and H. Y. Wang, “A robust combined algorithim of object tracking based on moving object detection,” in Proceedings of the International Conference on Intelligent Control and Information Processing (ICICIP '10), pp. 619–622, Dalian, China, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Su, G. Fang, and N. M. Kwok, “Adaptive colour feature identification in image for object tracking,” Mathematical Problems in Engineering, vol. 2012, Article ID 509597, 18 pages, 2012. View at Publisher · View at Google Scholar
  6. P. Han, J. Du, and M. Fang, “Spatial object tracking using an enhanced mean shift method based on perceptual spatial-space generation model,” Journal of Applied Mathematics, vol. 2013, Article ID 420286, 13 pages, 2013. View at Publisher · View at Google Scholar
  7. A. M. Sarhan, A. I. Saleh, and R. K. Elsadek, “A reliable event-driven strategy for real-time multiple object tracking using static cameras,” Advances in Multimedia, vol. 2011, Article ID 976463, 20 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. J. H. Yin, C. Y. Fu, and J. K. Hu, “Using incremental subspace and contour template for object tracking,” Journal of Network and Computer Applications, vol. 35, pp. 1740–1748, 2012. View at Publisher · View at Google Scholar
  9. 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
  10. O. Williams, A. Blake, and R. Cipolla, “A sparse probabilistic learning algorithm for real-time tracking,” in Proceedings of the 8th IEEE International Conference on Computer Vision, pp. 353–360, October 2003. View at Scopus
  11. Z. J. Han, J. B. Jiao, B. C. Zhang, Q. X. Ye, and J. Z. Liu, “Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR),” Pattern Recognition, vol. 44, no. 9, pp. 2170–2183, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. 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
  13. D. Wang, H. Lu, and X. Li, “Two dimensional principal components of natural images and its application,” Neurocomputing, vol. 74, no. 17, pp. 2745–2753, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. D. A. Ross, J. Lim, R. Lin, and M. 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
  15. Z. Kalal, J. Matas, and K. Mikolajczyk, “P-N learning: bootstrapping binary classifiers by structural constraints,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 49–56, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. 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
  17. B. Babenko, S. Belongie, and M. Yang, “Visual tracking with online multiple instance learning,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops '09), pp. 983–990, Miami Beach, Fla, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. 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, New York, NY, USA, June 2006. View at Publisher · View at Google Scholar · View at Scopus