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18th International Conference on Pattern Recognition (ICPR'06) Volume 4
Nonparametric Background Generation
Hong Kong
August 20-August 24
ISBN: 0-7695-2521-0
| ASCII Text | x | ||
| Yazhou Liu, Hongxun Yao, Wen Gao, Xilin Chen, Debin Zhao, "Nonparametric Background Generation," Pattern Recognition, International Conference on, vol. 4, pp. 916-919, 18th International Conference on Pattern Recognition (ICPR'06) Volume 4, 2006. | |||
| BibTex | x | ||
| @article{ 10.1109/ICPR.2006.868, author = {Yazhou Liu and Hongxun Yao and Wen Gao and Xilin Chen and Debin Zhao}, title = {Nonparametric Background Generation}, journal ={Pattern Recognition, International Conference on}, volume = {4}, year = {2006}, issn = {1051-4651}, pages = {916-919}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.868}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Pattern Recognition, International Conference on TI - Nonparametric Background Generation SN - 1051-4651 SP916 EP919 A1 - Yazhou Liu, A1 - Hongxun Yao, A1 - Wen Gao, A1 - Xilin Chen, A1 - Debin Zhao, PY - 2006 KW - null VL - 4 JA - Pattern Recognition, International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.868
A novel background generation method based on nonparametric background model is presented for background subtraction. We introduce a new model, named as effect components description (ECD), to model the variation of the background, by which we can relate the best estimate of the background to the modes (local maxima) of the underlying distribution. Based on ECD, an effective background generation method, most reliable background mode (MRBM), is developed. The basic computational module of the method is an old pattern recognition procedure, the mean shift, which can be used recursively to find the nearest stationary point of the underlying density function. The advantages of this method are three-fold: first, backgrounds can be generated from image sequence with cluttered moving objects; second, backgrounds are very clear without blur effect; third, it is robust to noise and small vibration. Extensive experimental results illustrate its good performance.
Citation:
Yazhou Liu, Hongxun Yao, Wen Gao, Xilin Chen, Debin Zhao, "Nonparametric Background Generation," icpr, vol. 4, pp.916-919, 18th International Conference on Pattern Recognition (ICPR'06) Volume 4, 2006
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