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Adaptive Gaussian Mixture Learning for High Traffic Region  

Park Dae-Yong (홍익대학 전기정보제어공학과)
Kim Jae-Min (홍익대학 전기정보제어공학과)
Cho Seong-Won (홍익대학 전기정보제어공학과)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.55, no.2, 2006 , pp. 52-61 More about this Journal
Abstract
For the detection of moving objects, background subtraction methods are widely used. An adaptive Gaussian mixture model combined with probabilistic learning is one of the most popular methods for the real-time update of the complex and dynamic background. However, probabilistic learning approach does not work well in high traffic regions. In this paper, we Propose a reliable learning method of complex and dynamic backgrounds in high traffic regions.
Keywords
GMM; Background Modeling; Motion Detection; Background Subtraction; High Traffic Region;
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