Table of Contents
ISRN Signal Processing
Volume 2011 (2011), Article ID 173176, 15 pages
http://dx.doi.org/10.5402/2011/173176
Research Article

Object Modelling and Tracking in Videos via Multidimensional Features

School of Computing and Mathematics, University of Western Sydney, NSW 1797, Australia

Received 30 November 2010; Accepted 5 January 2011

Academic Editor: C. S. Lin

Copyright © 2011 Zhuhan Jiang. 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. R. C. Gonzalez and R. E. Woods, Digital Image Processsing, Prentice-Hall, New York, NY, USA, 2nd edition, 2002.
  2. D. S. Lee, “Effective Gaussian mixture learning for video background subtraction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 827–832, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  3. A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proceedings of the IEEE, vol. 90, no. 7, pp. 1151–1162, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Miyoshi, J. K. Tan, and S. Ishikawa, “Extracting moving objects from a video by sequential background detection employing a local correlation map,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '08), pp. 3365–3369, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. A. K. Jain, Y. Zhong, and S. Lakshmanan, “Object matching using deformable templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 3, pp. 267–278, 1996. View at Google Scholar · View at Scopus
  6. F. Tombari, S. Mattoccia, L. Di Stefano, F. Regoli, and R. Viti, “A template analysis methodology to improve the efficiency of fast matching algorithms,” in Proceedings of the 11th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS '09), vol. 5807 of Lecture Notes in Computer Science, pp. 100–108, Bordeaux, France, September-October 2009. View at Publisher · View at Google Scholar
  7. T. Chateau and J.T. Lapreste, “Realtime kernel based machine learning template matching (KMLT),” Electronic Letters on Computer Vision and Image Analysis, vol. 8, no. 1, pp. 27–43, 2009. View at Google Scholar
  8. H. T. Nguyen and A. W. M. Smeulders, “Fast occluded object tracking by a robust appearance filter,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp. 1099–1104, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  9. M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” International Journal of Computer Vision, vol. 1, no. 4, pp. 321–331, 1987. View at Google Scholar
  10. N. Paragios and R. Deriche, “Geodesic active contours and level sets for the detection and tracking of moving objects,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 3, pp. 266–280, 2000. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Isard and A. Blake, “CONDENSATION—conditional density propagation for visual tracking,” International Journal of Computer Vision, vol. 29, no. 1, pp. 5–28, 1998. View at Google Scholar · View at Scopus
  12. C. Zimmer and J. C. Olivo-Marin, “Coupled parametric active contours,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 11, pp. 1838–1842, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  13. L. Pi, J. Fan, and C. Shen, “Color image segmentation for objects of interest with modified geodesic active contour method,” Journal of Mathematical Imaging and Vision, vol. 27, no. 1, pp. 51–57, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. Z. Ying, L. Guangyao, S. Xiehua, and Z. Xinmin, “Geometric active contours without re-initialization for image segmentation,” Pattern Recognition, vol. 42, no. 9, pp. 1970–1976, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. 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
  16. A. Elgammal, R. Duraiswami, and L. S. Davis, “Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 11, pp. 1499–1504, 2003. View at Publisher · View at Google Scholar
  17. Y. Zha, D. Bi, and Y. Yang, “Learning complex background by multi-scale discriminative model,” Pattern Recognition Letters, vol. 30, no. 11, pp. 1003–1014, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. T. J. Cham and J. M. Rehg, “Multiple hypothesis approach to figure tracking,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99), pp. 239–245, Ft. Collins, Colo, USA, June 1999. View at Scopus
  19. Y. Sheikh and M. Shah, “Bayesian modeling of dynamic scenes for object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 11, pp. 1778–1792, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  20. Z. Q. Huang and Z. Jiang, “Tracking camouflaged objects with weighted region consolidation,” in Proceedings of the Digital Imaging Computing: Techniques and Applications (DICTA '05), B. C. Lovell et al., Ed., pp. 161–168, IEEE CS, Cairns, Queensland, December 2005. View at Publisher · View at Google Scholar
  21. 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 PubMed · View at Scopus
  22. X. P. Zhang and M. D. Desai, “Segmentation of bright targets using wavelets and adaptive thresholding,” IEEE Transactions on Image Processing, vol. 10, no. 7, pp. 1020–1030, 2001. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  23. J. Bilmes, “A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden markov models,” Tech. Rep. TR-97-021, ICSI, 1997, http://citeseerx.ist.psu.edu/~viewdoc/summary?doi=10.1.1.28.613. View at Google Scholar
  24. A. Mason and Z. Duric, “Using histograms to detect and track objects in color video,” in Proceedings of Applied Imagery Pattern Recognition Workshop, pp. 154–159, 2001.
  25. M. Heikkilä and M. Pietikäinen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 657–662, 2006. View at Publisher · View at Google Scholar · View at PubMed
  26. A. Amer, “New binary morphological operations for effective low-cost boundary detection,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 17, no. 2, pp. 201–213, 2003. View at Publisher · View at Google Scholar