Moving target detection algorithm based on Gaussian mixture model

2013 ◽  
Author(s):  
Zhihua Wang ◽  
Du Kai ◽  
Xiandong Zhang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Target Detection ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Moving Target ◽  
Moving Target Detection

Moving Target Detection Based on Double Model

2014 ◽  
Vol 998-999 ◽  
pp. 759-762 ◽  
Author(s):  
Xin Ping Wu ◽  
Min Cang Fu
Keyword(s):  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Target Detection ◽  
Gaussian Mixture ◽  
Moving Target ◽  
Moving Target Detection ◽  
Invariant Region ◽  
Complex Region ◽  
Gaussian Mixture Modeling

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.

Moving target detection based on improved Gaussian mixture model considering camera motion

2019 ◽  
Vol 79 (11-12) ◽  
pp. 7005-7020 ◽  
Author(s):  
Enzeng Dong ◽  
Bo Han ◽  
Hao Jian ◽  
Jigang Tong ◽  
Zenghui Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Target Detection ◽  
Gaussian Mixture ◽  
Moving Target ◽  
Camera Motion ◽  
Moving Target Detection

Moving Target Detection Based on the Improved Gaussian Mixture Model Background Difference Method

2012 ◽  
Vol 482-484 ◽  
pp. 569-574
Author(s):  
Hong Liang Wang ◽  
Jin Qi Wang ◽  
Hai Fei Ding ◽  
Yang Wen Huang ◽  
Pan Liu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Target Detection ◽  
Difference Method ◽  
Gaussian Mixture ◽  
Moving Target ◽  
Moving Target Detection ◽  
Current Frame ◽  
Model Background ◽  
Background Difference

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.

Moving Object Detection Based on the Fish

2014 ◽  
Vol 644-650 ◽  
pp. 1253-1256 ◽  
Author(s):  
Lian Li ◽  
Jun Yi Song ◽  
Zhi Yang Yan
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Industrial Process ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Detection Algorithm ◽  
Moving Object ◽  
Moving Target ◽  
Target Fish ◽  
The Military

The detection and tracking of moving object is the important research of image analysis and understanding as well as in computer vision field, and have extensive application in the traffic monitoring, the military, industrial process control and medical research, but less application in the underwater monitoring of fish. In this paper, in order to be able to real-time detection of the fish in the digital video system moving target, proposed the fish moving target detection algorithm under a camera. With an improved background updating method of adaptive Gaussian mixture model, a method to detect the target fish based on Gaussian mixture model combined with edge detection operator.

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