Variational Bayesian learning for background subtraction based on local fusion feature

To resist the adverse effect of shadow interference, illumination changes, indigent texture and scenario jitter in object detection and improve performance, a background modelling method based on local fusion feature and variational Bayesian learning is proposed. First, U-LBSP (uniform-local binary...

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Bibliographic Details
Published in:	IET computer vision Vol. 10; no. 8; pp. 884 - 893
Main Authors:	Yan, Junhua, Wang, Shunfei, Xie, Tianxia, Yang, Yong, Wang, Jiayi
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 01.12.2016 Wiley
Subjects:	background modelling method background subtraction Bayes methods Bayesian analysis computational complexity distribution parameter optimisation expectation-maximisation algorithm Gaussian components Gaussian mixture model based on local fusion feature Illumination illumination changes image colour analysis image fusion image texture indigent texture Jitter lab colour learning (artificial intelligence) LFGMM location feature Mathematical models object detection optimisation Research Article Resists scenario jitter shadow disturbances shadow interference effect Shadows Surface layer Texture U-LBSP texture feature uniform-local binary similarity pattern texture feature variational Bayesian learning variational expectation maximisation algorithm variational techniques Gaussian mixture model based on local fusion feature image fusion distribution parameter optimisation object detection variational expectation maximisation algorithm illumination changes shadow disturbances optimisation variational techniques image colour analysis learning (artificial intelligence) lab colour location feature LFGMM uniform-local binary similarity pattern texture feature U-LBSP texture feature Gaussian components background modelling method shadow interference effect indigent texture image texture scenario jitter jitter variational Bayesian learning expectation-maximisation algorithm Bayes methods background subtraction computational complexity
ISSN:	1751-9632, 1751-9640, 1751-9640
Online Access:	Get full text
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