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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 925341, 8 pages
http://dx.doi.org/10.1155/2013/925341
Research Article

The Research and Application of Visual Saliency and Adaptive Support Vector Machine in Target Tracking Field

1School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing 210094, China
2School of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China
3Department of Computer Science and Technology, Hunan Vocational Institute of Safety & Technology, Changsha 410151, China

Received 15 September 2013; Accepted 4 November 2013

Academic Editor: Sabri Arik

Copyright © 2013 Yuantao Chen 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. F. F. Du, Particle Filter Object Tracking Algorithm Based on Vision and Its Application on Mobile Robot, Hangzhou Dianzi University, Hangzhou, China, 2009.
  2. G. Zhang, Z. Yuan, N. Zhang, X. Sheng, and T. Liu, “Visual saliency based on object tracking,” in Computer Vision—ACCV 2009, H. Zha, R.-I. Taniguchi, and S. Maybank, Eds., vol. 5995 of Lecture Notes in Computer Science, pp. 193–203, Springer, 2009. View at Publisher · View at Google Scholar
  3. P.-H. Li, “A novel color based particle filter algorithm for object tracking,” Chinese Journal of Computers, vol. 32, no. 12, pp. 2454–2463, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Pu, F. Zhou, and X. Bai, “Particle filter based on color feature with contour information adaptively integrated for object tracking,” in Proceedings of the 4th International Symposium on Computational Intelligence and Design (ISCID '11), pp. 359–362, Zhejiang University, Hangzhou, China, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Xia, X. J. Wu, and Z. H. Feng, “Mean shift algorithm for visual tracking based on feature contribution,” Control and Decision, vol. 27, no. 7, pp. 1021–1026, 2012. View at Google Scholar
  6. Z. Jiang, Z. Lin, and L. S. Davis, “Label consistent K-SVD: learning a discriminative dictionary for recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp. 2651–2664, 2013. View at Google Scholar
  7. V. Badrinarayanan, I. Budvytis, and R. Cipolla, “Semi-supervised video segmentation using tree structured graphical models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp. 2751–2764, 2013. View at Publisher · View at Google Scholar
  8. Z. H. Zeng, C. L. Zhou, K. H. Lin et al., “Visual attention computational model based on tracking target,” Computer Engineering, vol. 34, no. 23, pp. 241–243, 2008. View at Google Scholar
  9. G. Yang and H. Liu, “Visual attention & multi-cue fusion based human motion tracking method,” in Proceedings of the 6th International Conference on Natural Computation (ICNC '10), pp. 2044–2054, Yantai University, Yantai, China, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Desire, F. David, and M. Fabrice, “Using visual saliency for object tracking with particle filters,” in Proceedings of the 18th European Signal Processing Conference (EUSIPCO '10), Aalborg University, Aalborg, Denmark, August 2010.
  11. Y. Zhang, Z.-L. Zhang, Z.-K. Shen, and X.-Y. Lu, “The images tracking algorithm using particle filter based on dynamic salient features of targets,” Acta Electronica Sinica, vol. 36, no. 12, pp. 2306–2311, 2008. View at Google Scholar · View at Scopus
  12. G. Cauwenberghs and T. Paggio, Incremental and Decremental Support Vector Machine Learning, vol. 13 of Advances in Neural Information Processing, MIT Press, 2001.
  13. S. He, Q. Yang, R. W. H. Lau et al., “Visual tracking via locality sensitive histograms,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 2427–2434, 2013.
  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. J. Yan, M. Zhu, H. Liu, and Y. Liu, “Visual saliency detection via sparsity pursuit,” IEEE Signal Processing Letters, vol. 34, no. 9, pp. 739–742, 2010. View at Google Scholar
  16. L. Xu, J. Du, and Q. Li, “Image fusion based on nonsubsampled contourlet transform and saliency-motivated pulse coupled neural networks,” Mathematical Problems in Engineering, vol. 2013, Article ID 135182, 10 pages, 2013. View at Publisher · View at Google Scholar
  17. K. Madani, D. M. Ramik, and C. Sabourin, “Multilevel cognitive machine-learning-based concept for artificial awareness: application to Humanoid robot awareness using visual saliency,” Applied Computational Intelligence and Soft Computing, vol. 2012, Article ID 354785, 11 pages, 2012. View at Publisher · View at Google Scholar