文章基本信息

标题：A Background Modeling Algorithm Based on Improved Adaptive Mixture Gaussian
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作者：Han, Ming ; Liu, Jiaomin ; Sun, Yi 等
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2013
卷号：8
期号：9
页码：2239-2244
DOI：10.4304/jcp.8.9.2239-2244
语种：English
出版社：Academy Publisher
摘要：For better background modeling in scenes with nonstationary background, a background modeling algorithm based on adaptive parameter adjustment of the Mixture Gaussian is proposed. Mixture Gaussians is applied to learn the distribution of per-pixel in the temporal domain and to control the adaptive adjustment of number K of Gaussian components through increasing, deleting or merging similar Gaussian components adaptively. The new parameters C_k and φ_k are introduced in the adaptive parameter model. According to the actual situation, the adaptive adjustment of ρ can accurate track the real-time changes with the pixel, which improves the robustness and convergence. Experimental results show that the algorithm can rapidly response when the scene changes in the sequence of video with many uncertain factors, and realize adaptive background modeling with accurate target detection.
关键词：Gaussian Mixture Model;background modeling;adaptive adjustment K-ρ;moving target detection