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- W2348474677 abstract "Gaussian mixture model is a widely used approach for background modeling to detect moving objects from static cameras. Base on the situation that traditional Gaussian mixture model could not effictively solve the problems such as poor adaptability of illumination change and single of learning rate,this paper proposed a method to detect the illumination variation and update the learning rate,in order to achieve the purpose of the adaptive updating background model. Firstly,the proposed method used the color histogram matching algorithm to introduce the factor of illumination variation. At the same time,it also introduced a counter of the updating parameters of model to adaptively adjust learning rates. And then,adaptive selection of the number of components in the model reduced the computation time and enhanced the real-time nature of the system. The experimental results show that this proposed method can quickly and effectively adapt to changes in the scene,and has better robustness and stability than traditional Gaussian mixture model." @default.
- W2348474677 created "2016-06-24" @default.
- W2348474677 creator A5060430550 @default.
- W2348474677 date "2013-01-01" @default.
- W2348474677 modified "2023-10-07" @default.
- W2348474677 title "Improved algorithm of adaptive Gaussian mixture model for real-time moving object detection" @default.
- W2348474677 hasPublicationYear "2013" @default.
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