Advanced search
Publication Cover
The Imaging Science Journal Volume 64, 2016 - Issue 2
Submit an article Journal homepage
114
Views
4
CrossRef citations to date
0
Altmetric
Original Articles

Dynamic foreground detection based on improved Codebook model

H. ZhouCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing211106, PR China
,
G. SuInstitute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing100084, PR China
&
X. JiangCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing211106, PR ChinaCorrespondence[email protected]
Pages 109-117 | Received 15 Jun 2015, Accepted 19 Jan 2016, Published online: 08 Mar 2016

References

  • Wan, Y., Han, Y. and Lu, H. Q. The methods for moving object detection. Comput. Simul., 2006, 23, 221–226.
  • Mittal, A., Paragios, N. and Ramesh, V. Background modeling and subtraction of dynamic scenes, Proceedings of ninth IEEE International conference on computer vision, 2003, IEEE, pp. 1305–1312.
  • Kim, K., Chalidabhongse, T. H., Harwood, D. and Davis, L. Background modeling and subtraction by codebook construction. 2004 International conference on image processing, Vol. 5, 2004, pp. 3061–3064.
  • Ko, T., Soatto, S. and Estrin, D. Background subtraction on distributions. In: Proceedings of European conference on computer vision, Marseille, France 2008, pp. 276–89.
  • Dalley, G., Migdal, J. and Grimson, W. E. L. Background subtraction for temporally irregular dynamic textures. IEEE workshop on applications of computer vision, Copper Mountain 2008, pp. 1–7.
  • Liang, G. S. and Zhu, X. C. Improved motion body detection algorithm based on frame difference. Video Appl. Proj., 2009, 33, 133–135.
  • Wu, M. J. and Peng, X. R. Spatio-temporal context for codebook-based dynamic background subtraction. AEU-Int. J. Electron. Commun., 2010, 64, 739–747. doi: 10.1016/j.aeue.2009.05.004
  • Dai, J. P. and Han, J. P. Multi-layer foreground detection based on photometric invariant color. J. Mech. Electr. Eng., 2010, 27, 28–31.
  • Feng, H. W., Gong, S. R. and Liu, C. P. Foreground detection based on improved Gaussian mixture model. Comput. Eng., 2011, 37, 179–182.
  • Rahimizadeh, H., Marhaban, M. H., Kamil, R. M. and Ismail, N. Color image segmentation based on Bayesian theorem and kernel density estimation. Eur. J. Sci. Res., 2009, 26, (3), 430–436.
  • Elgammal, A., Duraiswami, R., Harwood, D. and Davis, L. S. Background and foreground modeling using non-parametric kernel density estimation for video surveillance. Proc. IEEE. 2002, 90, (7), 1151–1163.
  • Han, B., Comaniciu, D. and Davis, L. Sequential kernel density approximation through mode propagation: applications to background modeling. Proc. of Asian Conf. on Computer Vision, Jeju Island, Korea, 2004.
  • Ye, F., Fan, M. M. and Deng, Z. H. An improvement of modeling objective detection algorithm based on average background model. J. Fujian Normal Univ. (Nat. Sci. Ed.), 2011, 27, 44–49.
  • Su, S. T. and Chen, Y. Y. Moving object segmentation using improved running Gaussian average background model. IEEE Digital Image Comput. Techn. Appl., 2008, 15, 24–31.
  • Chen, R., Deng, Y., Xiang, S. M. and Li, H. A non-parametric foreground/background segment method by fusion of intensity and edge feature. J. Comput.-aided Des. Comput. Graph., 2005, 17, 1278–1284.
  • Hsu, R.-L., Abdel-Mottable, M. and Jain, A. K. Face detection in color images. IEEE Trans. Pattern Anal. Machine Intell., 2002, 24, (5), 696–706. doi: 10.1109/34.1000242
  • Kim, K., Chalidabhongse, T. H., Harwood, D. and Davis, L. Real-time foreground–background segmentation using codebook model. Real-time Imaging, 2005, 11, 172–185. doi: 10.1016/j.rti.2004.12.004

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.