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- W2096006988 abstract "Gaussian mixture models (GMM) is used to represent the dynamic background in a surveillance video to detect the moving objects automatically. All the existing GMM based techniques inherently use the proportion by which a pixel is going to observe the background in any operating environment. In this paper we first show that such a proportion not only varies widely across different scenarios but also forbids using very fast learning rate. We then propose a dynamic background generation technique in conjunction with basic background subtraction which detected moving objects with improved stability and superior detection quality on a wide range of operating environments in two sets of benchmark surveillance sequences." @default.
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- W2096006988 date "2008-09-01" @default.
- W2096006988 modified "2023-09-29" @default.
- W2096006988 title "On Stable Dynamic Background Generation Technique Using Gaussian Mixture Models for Robust Object Detection" @default.
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- W2096006988 doi "https://doi.org/10.1109/avss.2008.12" @default.
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