A Novel Clustering-Based Method for Adaptive Background Segmentation

CC
CRV
2006
The 3rd Canadian Conference on Computer and Robot Vision (CRV'06)
Abstract - A Novel Clustering-Based Method for Adaptive Background Segmentation

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The 3rd Canadian Conference on Computer and Robot Vision (CRV'06)

Quebec City, Quebec, Canada

June 07-June 09

ISBN: 0-7695-2542-3

	ASCII Text	x
	S. Indupalli, M.A. Ali, B. Boufama, "A Novel Clustering-Based Method for Adaptive Background Segmentation," Computer and Robot Vision, Canadian Conference, pp. 37, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06), 2006.

	BibTex	x
	@article{ 10.1109/CRV.2006.5, author = {S. Indupalli and M.A. Ali and B. Boufama}, title = {A Novel Clustering-Based Method for Adaptive Background Segmentation}, journal ={Computer and Robot Vision, Canadian Conference}, volume = {0}, year = {2006}, isbn = {0-7695-2542-3}, pages = {37}, doi = {http://doi.ieeecomputersociety.org/10.1109/CRV.2006.5}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer and Robot Vision, Canadian Conference TI - A Novel Clustering-Based Method for Adaptive Background Segmentation SN - 0-7695-2542-3 SP EP A1 - S. Indupalli, A1 - M.A. Ali, A1 - B. Boufama, PY - 2006 KW - null VL - 0 JA - Computer and Robot Vision, Canadian Conference ER -

S. Indupalli, University of Windsor, Ontario, Canada

M.A. Ali, University of Windsor, Ontario, Canada

B. Boufama, University of Windsor, Ontario, Canada

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CRV.2006.5

This paper presents a new histogram-based method for dynamic background modeling using a sequence of images extracted from video. In particular, a k-means clustering technique has been used to identify the foreground objects. Because of its shadow resistance and discriminative properties, we have used images in the HSV color space instead of the traditional RGB color space. The experimental results on real images are very encouraging as we were able to retrieve perfect backgrounds in simple scenes. In very complex scenes, the backgrounds we have obtained were very good. Furthermore, our method is very fast and could be used in real-time applications after optimization.

Citation:

S. Indupalli, M.A. Ali, B. Boufama, "A Novel Clustering-Based Method for Adaptive Background Segmentation," crv, pp.37, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06), 2006

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