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Mathematical Problems in Engineering
Volume 2015, Article ID 376494, 12 pages
http://dx.doi.org/10.1155/2015/376494
Research Article

Robust Object Tracking Based on Simplified Codebook Masked Camshift Algorithm

1Information Research Institute, Shandong Academy of Sciences, Jinan 250014, China
2Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3School of Information Science and Engineering, Shandong University, Jinan 250100, China

Received 26 January 2015; Revised 5 June 2015; Accepted 10 June 2015

Academic Editor: Fernando Torres

Copyright © 2015 Yuanyuan Zhang 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. T. Meier and K. N. Ngan, “Video segmentation for content-based coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 9, no. 8, pp. 1190–1203, 1999. View at Publisher · View at Google Scholar · View at Scopus
  2. J. L. Barron, D. J. Fleet, and S. S. Beauchemin, “Performance of optical flow techniques,” International Journal of Computer Vision, vol. 12, no. 1, pp. 43–77, 1994. View at Publisher · View at Google Scholar · View at Scopus
  3. A. M. Elgammal, D. Hanvood, and L. S. Davis, “Non-parametric model for background subtraction,” in Computer Vision—ECCV 2000: 6th European Conference on Computer Vision Dublin, Ireland, June 26–July 1, 2000 Proceedings, Part II, vol. 1843 of Lecture Notes in Computer Science, pp. 751–767, Springer, Berlin, Germany, 2000. View at Publisher · View at Google Scholar
  4. N. Friedman and S. Russell, “Image segmentation in video sequences: a probabilistic approach,” in Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence, pp. 175–181, Morgan Kuafmann, Providence, RI, USA, 1997. View at Google Scholar
  5. K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground-background segmentation using codebook model,” Real-Time Imaging, vol. 11, no. 3, pp. 172–185, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Wu and X. Peng, “Spatio-temporal context for codebook-based dynamic background subtraction,” AEU–International Journal of Electronics and Communications, vol. 64, no. 8, pp. 739–747, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. Q. Tu, Y. Xu, and M. Zhou, “Box-based codebook model for real-time objects detection,” in Proceedings of the 7th World Congress on Intelligent Control and Automation (WCICA '08), pp. 7621–7625, IEEE, Chongqing, China, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. K. Fukunaga and L. D. Hostetler, “The estimation of the gradient of a density function, with applications in pattern recognition,” IEEE Transactions on Information Theory, vol. 21, no. 1, pp. 32–40, 1975. View at Google Scholar · View at MathSciNet
  9. Y. Cheng, “Mean shift, mode seeking, and clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 790–799, 1995. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Comaniciu and P. Meer, “Robust analysis of feature spaces: color image segmentation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 750–755, IEEE, San Juan, Puerto Rico, June 1997. View at Publisher · View at Google Scholar
  11. B. Bradski G, “Real time face and object tracking as a component of a perceptual user interface,” in Proceedings of the 4th IEEE Workshop on Applications of Computer Vision, pp. 214–219, October 1998.
  12. S. Birchfield Stanley and S. Rangarajan, “Spatiograms versus histograms for region-based tracking,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 1158–1163, Institute of Electrical and Electronics Engineers Computer Society, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. C. Yang, R. Duraiswami, and L. Davis, “Efficient mean-shift tracking via a new similarity measure,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 176–183, IEEE, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Xiao and X. Gang, “Camshift ship tracking algorithm based on multi-feature adaptive fusion,” Opto-Electronic Engineering, vol. 38, no. 5, pp. 52–58, 2011 (Chinese). View at Publisher · View at Google Scholar · View at Scopus
  15. C. Yang, R. Duraiswami, D. DeMenthon, and L. Davis, “Mean-shift analysis using quasi-Newton methods,” in Proceedings of the International Conference on Image Processing (ICIP '03), vol. 2, pp. 447–450, IEEE Computer Society, Barcelona, Spain, September 2003. View at Publisher · View at Google Scholar
  16. M. Á. Carreira-Perpiñán, “Acceleration strategies for Gaussian mean-shift image segmentation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1160–1167, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. J.-Y. Zuo, Y. Liang, Q. Pan, C.-H. Zhao, and H.-C. Zhang, “Camshift tracker based on multiple color distribution models,” Acta Automatica Sinica, vol. 34, no. 7, pp. 736–742, 2008 (Chinese). View at Publisher · View at Google Scholar · View at Scopus
  18. K. Sun and S. Liu, “Combined algorithm with modified camshift and kalman filter for multi-object tracking,” Information and Control, vol. 38, no. 1, pp. 9–14, 2009 (Chinese). View at Google Scholar
  19. P. Juanchun, G. Lizhong, and S. Jianbo, “The hand tracking for humanoid robot using Camshift algorithm and Kalman filter,” Journal of Shanghai Jiaotong University, vol. 40, no. 7, pp. 1161–1165, 2006 (Chinese). View at Google Scholar
  20. L. Yuan, L. Ling, Z. Baisheng, and Y. Hongmei, “Video hand tracking algorithm based on hybrid Camshift and Kalman filter,” Application Research of Computers, vol. 26, no. 3, pp. 1163–1165, 2009 (Chinese). View at Google Scholar
  21. X. Liu, F. Chang, and H. Wang, “An object tracking method based on improved Camshift algorithm,” Microcomputer Information, vol. 23, pp. 297–298, 2007 (Chinese). View at Google Scholar
  22. W. Xiaojuan, Z. Haiting, W. Lei, and X. Liqun, “An improved Camshift hand tracking algorithm,” Journal of Shangdong University (Engineering Science), vol. 34, no. 6, pp. 120–124, 2004 (Chinese). View at Google Scholar
  23. C. Wang, Intelligent Monitoring of Moving Object Detection and Tracking Algorithms, Beijing University of Posts and Telecommunications, Beijing, China, 2012, (Chinese).
  24. http://www.multitel.be/image/research-development/research-projects/candela.php.
  25. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallflower: principles and practice of background maintenance,” in Proceedings of the 7th IEEE International Conference on Computer Vision (ICCV' 99), pp. 255–261, September 1999. View at Scopus
  26. http://www.stats.ox.ac.uk/~wauthier/tracker/pets2000.html.
  27. 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
  28. K. Zhang, L. Zhang, and M.-H. Yang, “Real-time compressive tracking,” in Computer Vision—ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7–13, 2012, Proceedings, Part III, vol. 7574 of Lecture Notes in Computer Science, pp. 864–877, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  29. Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking learning detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 1, pp. 1–14, 2010. View at Google Scholar