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Nonparametric Background Generation
Hong Kong August 20-August 24
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.86818th International Conference on Patt ...
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Yazhou Liu, Harbin Institute of Technology, China
Hongxun Yao, Harbin Institute of Technology, China
Wen Gao, Chinese Academy of Science Beijing 100080, P.R China
Xilin Chen, Chinese Academy of Science Beijing 100080, P.R China
Debin Zhao, Chinese Academy of Science Beijing 100080, P.R China
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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