NDLI: Video background subtracion using improved Adaptive-K Gaussian Mixture Model

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2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE)

Volume 6 - 2010

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An approach to refactoring legacy systems

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Video background subtracion using improved Adaptive-K Gaussian Mixture Model

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Volume 1 - 2010

2008 International Conference on Advanced Computer Theory and Engineering

Video background subtracion using improved Adaptive-K Gaussian Mixture Model

Content Provider	IEEE Xplore Digital Library
Author	Hao Zhou Xuejie Zhang Yun Gao Pengfei Yu
Copyright Year	2010
Description	Author affiliation: Information School of Yunnan University, Kunming China (Hao Zhou; Xuejie Zhang; Yun Gao; Pengfei Yu)
Abstract	Video stream segmentation is a critical step in many computer vision applications. Background subtraction based on Gaussian Mixture Model (GMM) is a commonly used technique for video segmentation. In this paper, an improved Adaptive-K Gaussian Mixture Model (AKGMM) method was presented for updating background. The dimension of the parameter space at each pixel can be adjusted adaptively according to the frequency of pixel value changes. The number of GMM reflected the complexity of pattern at the pixel. Experimental results demonstrated that the proposed method is more adaptive and robust than some existing approaches.
File Size	735912
File Format	PDF
ISBN	9781424465392
ISSN	21547491
e-ISBN	9781424465422
DOI	10.1109/ICACTE.2010.5579536
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Background Subtraction Adaptation model Adaptive-K Gaussian Mixture Model
Content Type	Text
Resource Type	Article

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