Nonparametric Background Generation

Authors:
Yazhou Liu;Hongxun Yao;Wen Gao;Xilin Chen;Debin Zhao
Affiliations:
Harbin Institute of Technology, China;Harbin Institute of Technology, China;Chinese Academy of Science Beijing 100080, P.R China;Chinese Academy of Science Beijing 100080, P.R China;Chinese Academy of Science Beijing 100080, P.R China
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Year:
2006

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Abstract

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.