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http://dx.doi.org/10.5391/JKIIS.2008.18.3.387

Layered Object Detection using Adaptive Gaussian Mixture Model in the Complex and Dynamic Environment  

Lee, Jin-Hyung (홍익대학교 전기정보제어공학과)
Cho, Seong-Won (홍익대학교 전기정보제어공학과)
Kim, Jae-Min (홍익대학교 전기정보제어공학과)
Chung, Sun-Tae (숭실대학교 정보통신전자공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.18, no.3, 2008 , pp. 387-391 More about this Journal
Abstract
For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.
Keywords
Object Detection; Layer; Complex Environment Modeling; Gaussian Mixture Model;
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