scholarly journals Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring

2006 ◽  
Vol 55 (5) ◽  
pp. 699-715 ◽  
Author(s):  
Tao Chen ◽  
Julian Morris ◽  
Elaine Martin
Keyword(s):  
Probability Density ◽  
Density Estimation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Process Monitoring ◽  
Gaussian Mixture ◽  
Probability Density Estimation ◽  
Statistical Process ◽  
Model Application ◽  
Statistical Process Monitoring

Research on Background Modeling by Gaussian Mixture Model without Parameter Adjustment

2013 ◽  
Vol 380-384 ◽  
pp. 1394-1397
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Density Estimation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Mean Shift ◽  
Gaussian Mixture ◽  
Background Modeling ◽  
Background Model ◽  
Probability Density Estimation ◽  
Mean Shift Algorithm ◽  
Statistical Background

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.

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