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- W2039163852 abstract "Adaptive Gaussian mixture learning has been used for moving object detection in video surveillance applications for years. However, the method suffers from low convergence speed in the learning process, especially in complex environments. This paper proposed a novel method which improves adaptive Gaussian mixture leaning from four aspects including calculating the learning rate of means and variances respectively, employing a default minimal value for variances, selecting the optimal match for new pixel and improving renewal equation of weights. Experimental results show that our algorithm is promising, compared with conventional methods." @default.
- W2039163852 created "2016-06-24" @default.
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- W2039163852 date "2010-10-01" @default.
- W2039163852 modified "2023-09-26" @default.
- W2039163852 title "Adaptive Gaussian mixture learning for moving object detection" @default.
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- W2039163852 doi "https://doi.org/10.1109/icbnmt.2010.5705275" @default.
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