Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background

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Title Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background
Author Zhang, Baochang; Gao, Yongsheng; Zhao, Sanqiang; Zhong, Bineng
Journal Name IEEE Transactions on Circuits and Systems for Video Technology
Editor Editor-in-Chief: Hamid Gharavi
Year Published 2011
Place of publication United States
Publisher IEEE Circuits and Systems Society
Abstract This paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for background modeling and motion detection in complex and dynamic environments. The texture pattern flow encodes the binary pattern changes in both spatial and temporal neighborhoods. The integral histogram of texture pattern flow is employed to extract the discriminative features from the input videos. Different from existing uniform threshold based motion detection approaches which are only effective for simple background, the kernel similarity modeling is proposed to produce an adaptive threshold for complex background. The adaptive threshold is computed from the mean and variance of an extended Gaussian mixture model. The proposed KSM-TPF approach incorporates machine learning method with feature extraction method in a homogenous way. Experimental results on the publicly available video sequences demonstrate that the proposed approach provides an effective and efficient way for background modeling and motion detection.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1109/TCSVT.2011.2105591
Copyright Statement Copyright 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Volume 21
Issue Number 1
Page from 29
Page to 38
ISSN 1051-8215
Date Accessioned 2011-03-04
Date Available 2011-10-18T07:26:16Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Computer Vision; Pattern Recognition and Data Mining
URI http://hdl.handle.net/10072/40128
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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