Research on Background Modeling by Gaussian Mixture Model without Parameter Adjustment

Abstract:

When modeling background model by Gaussian mixture model, there exist the defects that parameters can not be updated adaptively. In this paper, we adopt mean-shift algorithm to overcome these defects. Firstly, this paper introduces the initialized parameters, such as variance, mean, and weights and others, when modeling and then the parameters are constantly adjusted in the subsequent calculations. Then the statistical background model based on probability density estimation is put forward and using mean-shift algorithm updates the parameters adaptively. At last, the algorithm of mixture Gaussian background modeling method based on mean-shift is implemented. The experimental results show that the algorithm can effectively update parameters adaptively and the obtained background model is better.

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 1394-1397
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.1394
• Online since: August 2013
• Authors: Hong Hai Liu, Xiang Hua Hou
• Keywords: Background Modeling, Gaussian Mixture Model, Mean-Shift

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