NDLI: Adaptive fuzzy apporach to background modeling using PSO and KLMS

Content Provider	IEEE Xplore Digital Library
Author	Zilong Li Weiming Liu Yang Zhang
Copyright Year	2012
Description	Author affiliation: School of Civil Engineering and Transportion, South China University of Technology, Guangzhou, Guangdong Province, China (Zilong Li; Weiming Liu; Yang Zhang)
Abstract	This paper presents a new adaptive fuzzy approach for background estimation in video sequences of complex scene from the function estimation point of view. A Takagi-Sugeno-Kang (TSK) type fuzzy system is used as the function estimator in the study. The proposed approach uses a hybrid learning method combining both the particle swarm optimization (PSO) and the Kernel Least Mean Square (KLMS) to train the fuzzy estimator. In order to estimate background, we first interpret foreground samples as outliers relative to the background ones and so propose an Outlier Separator (OS). Then, the obtained results of OS algorithm are employed in the fuzzy estimator in order to train and estimate background in each pixel. Experimental results show the high accuracy and effectiveness of the proposed method in background estimation and foreground detection for various scenes.
Starting Page	4601
Ending Page	4607
File Size	256566
Page Count	7
File Format	PDF
ISBN	9781467313971
e-ISBN	9781467313988
DOI	10.1109/WCICA.2012.6359351
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Training Kernel Adaptation models Fuzzy systems Vectors Learning systems outlier separator(OS) background modeling Takagi-Sugeno-Kang(TSK) fuzzy system particle swarm optimization (PSO) Kernel Least Mean Square (KLMS)
Content Type	Text
Resource Type	Article

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