Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes

2007 ◽  
Vol 16 (2) ◽  
pp. 023013 ◽  
Author(s):  
Wei Zhang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Dynamic Scenes

Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model Using XYZ Color Model

2013 ◽  
Vol 347-350 ◽  
pp. 3505-3509 ◽  
Author(s):  
Jin Huang ◽  
Wei Dong Jin ◽  
Na Qin
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Three Dimensional ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Color Model ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Model Algorithm

In order to reduce the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance, a moving objects detection algorithm based on three-dimensional Gaussian mixture codebook model using XYZ color model is proposed. In this algorithm, a codebook model based on XYZ color model is built, and then the Gaussian model based on X, Y and Z components in codewords is established respectively. In this way, the characteristic of the three-dimensional Gaussian mixture model for the codebook model is obtained. The experimental results show that the proposed algorithm can attain higher real-time capability and its average frame rate is about 16.7 frames per second, while it is about 8.3 frames per second for the iGMM (improved Gaussian mixture model) algorithm, about 6.1 frames per second for the BM (Bayes model) algorithm, about 12.5 frames per second for the GCBM (Gaussian-based codebook model) algorithm, and about 8.5 frames per second for the CBM (codebook model) algorithm in the comparative experiments. Furthermore the proposed algorithm can obtain better detection quantity.

scholarly journals Adaptive threshold for moving objects detection using gaussian mixture model

2020 ◽  
Vol 18 (2) ◽  
pp. 1122
Author(s):  
Moch Arief Soeleman ◽  
Aris Nurhindarto ◽  
Muslih Muslih ◽  
Karis W. ◽  
Muljono Muljono ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Adaptive Threshold ◽  
Moving Objects Detection ◽  
Objects Detection

Moving Objects Detection Based on Gaussian Mixture Model and Saliency Map

2011 ◽  
Vol 63-64 ◽  
pp. 350-354 ◽  
Author(s):  
Li Li Lin ◽  
Neng Rong Chen
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Visual Saliency ◽  
Gaussian Mixture ◽  
Saliency Map ◽  
Dynamic Background ◽  
Moving Objects Detection ◽  
Saliency Maps ◽  
Objects Detection

The background modeling method based on the Gaussian mixture model (GMM) is usually used to detect the moving objects in static background. But when applied to dynamic background, for example caused by camera jitter, the wrong detection rate of moving objects is high, and thus affects the follow-up tracking. In addition, the method with GMM can not effectively remove the moving objects shadow region. This paper proposes a moving object detection method based on GMM and visual saliency maps, which not only can remove the disturbance caused by camera jitter, but also can effectively solve the shadow problem and achieve stable moving objects detection.

A Moving Objects Detection Method Base on Improved GMM

2011 ◽  
Vol 225-226 ◽  
pp. 637-641
Author(s):  
Yi Bing Li ◽  
He Jiang Jia ◽  
Ao Li
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
False Detection ◽  
Moving Objects Detection ◽  
Method Base ◽  
Matching Mechanism ◽  
Computer Vision Applications ◽  
Objects Detection

Motion detection is the first and important step in many computer vision applications. Gaussian mixture model is an effective way for moving objects detection, but there are some shortcomings of this model such as slow updating rate and false detection in complex background. In this paper, we proposed an improved Gaussian mixture model method. A matching distance is defined to compute the learning rate when updating the models, and we also use dual threshold to improve the matching mechanism. Experimental results show that this method can get a faster adaptation to background and better contour of the moving objects.

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