Spatio-temporal Gaussian Mixture Model for Background Modeling

2012 ◽  
Author(s):  
Youngsung Soh ◽  
Yongsuk Hae ◽  
Intaek Kim
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Background Modeling ◽  
Spatio Temporal

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.

Research on GMM Background Modeling and its Covariance Estimation

2011 ◽  
Vol 383-390 ◽  
pp. 2327-2333
Author(s):  
Yun Chu Zhang ◽  
Ru Min Zhang ◽  
Shi Jun Song
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Background Modeling ◽  
Covariance Estimation ◽  
Parameters Estimation ◽  
Convergence Problem ◽  
Slow Convergence ◽  
Segmentation Accuracy ◽  
The Stability

This paper analyzes the background modeling mechanism using Gaussian mixture model and the stability /plasticity dilemma in parameters estimation of GMM background model. To solve the slow convergence problem of Gaussian mean and covariance update formula given by Stauffer, a new updating strategy is proposed, which weighs the model adaptability and motion segmentation accuracy. Experiments show that the proposed algorithm improves the accuracy of modal learning and speed of covariance convergence.

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