Moving target detection method based on improved Gaussian mixture model

Author(s):  
J. Y. Ma ◽  
F. R. Jie ◽  
Y. J. Hu
2014 ◽  
Vol 998-999 ◽  
pp. 759-762 ◽  
Author(s):  
Xin Ping Wu ◽  
Min Cang Fu

In order to overcome the problem that the single-Gauss model is poor of anti-interference and Gaussian Mixture model is poor of real-time, we present the double modeling algorithm of moving target detection. We use three frame difference method to distinguish the invariant region and complex region in background. And then we use single-Gauss modeling to model the invariant background while the complex region of background would be modeled with Gaussian Mixture modeling. It is more effective than the single-Gauss model and more efficient than the Gaussian Mixture model .The experimental results show that the improved algorithm is superior to the traditional single Gauss model or Gaussian Mixture model. It can detect moving target more quickly and accurately, with good robustness and real-time.


2019 ◽  
Vol 79 (11-12) ◽  
pp. 7005-7020 ◽  
Author(s):  
Enzeng Dong ◽  
Bo Han ◽  
Hao Jian ◽  
Jigang Tong ◽  
Zenghui Wang

2012 ◽  
Vol 482-484 ◽  
pp. 569-574
Author(s):  
Hong Liang Wang ◽  
Jin Qi Wang ◽  
Hai Fei Ding ◽  
Yang Wen Huang ◽  
Pan Liu

Gaussian mixture model background difference method is an effective method to achieve the moving target detection. According to its deficiencies of accuracy, speed and other aspects, this paper presents an improved Gaussian mixture model background difference method. Firstly, use three-frame difference method to detect the alterant area rapidly by the advantages of accuracy and fast speed. Then, use the area method to judge the results, and determine whether it is need for target extraction of the current frame by Gaussian mixture model background method, which can reduce the time of object detecting and background modeling. Meanwhile, the update strategies of the Gaussian mixture model background is improved, which can further enhance the detective accuracy and speed for the large and slow moving targets.


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