Toward a kindergarten video surveillance system (KVSS) using background subtraction based Type-2 FGMM model

Soft Computing and Pattern Recognition(2014)

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摘要
This paper presents a new video surveillance system called KVSS using background based on Type-2 Fuzzy Gaussian Mixture Models (T2 FGMMs). These techniques are used for resolving some limitations on Gaussian Mixture Models (GMMs) techniques on critical situations like moved camera jitter, illumination changes and objects being introduced or removed from the scene. In this context, we introduce descriptions of T2 GMMs and we present an experimental validation using a new evaluation video dataset which presents various problems. Results demonstrate the relevance of the proposed system.
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关键词
Gaussian processes,fuzzy set theory,video surveillance,Gaussian mixture model techniques,KVSS,T2 FGMM,background subtraction based type-2 FGMM model,camera jitter,illumination,kindergarten video surveillance system,video dataset,Background Subtraction,Human localization,Human tracking,T2 FGMMs
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