Open Access
Fuzzy Running Average and Fuzzy Background Subtraction: Concepts and Application
Mohamad Hoseyn Sigari,Naser Mozayani,Hamid Reza Pourreza +2 more
- Vol. 8
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TLDR
Experimental results show that fuzzy approach for background modeling and background subtraction is relatively more accurate than classical approach and this method is suggested for fuzzy background modeling.Abstract:
Summary Running average method and its modified version are two simple and fast methods for background modeling. In this paper, some weaknesses of running average method and standard background subtraction are mentioned. Then, a fuzzy approach for background modeling and background subtraction is proposed. For fuzzy background modeling, fuzzy running average is suggested. Background modeling and background subtraction algorithms are very commonly used in vehicle detection systems. To demonstrate the advantages of fuzzy running average and fuzzy background subtraction, these methods and their standard versions are compared in vehicle detection application. Experimental results show that fuzzy approach is relatively more accurate than classical approach.read more
Citations
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Journal ArticleDOI
Traditional and recent approaches in background modeling for foreground detection: An overview
TL;DR: The purpose of this paper is to provide a complete survey of the traditional and recent approaches to background modeling for foreground detection, and categorize the different approaches in terms of the mathematical models used.
Journal ArticleDOI
A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos
Andrews Sobral,Antoine Vacavant +1 more
TL;DR: From this large set of various BG methods, a relevant experimental analysis is conducted to evaluate both their robustness and their practical performance in terms of processor/memory requirements.
Journal ArticleDOI
Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey
TL;DR: The purpose of this paper is to provide a survey and an original classification of improvements of the original MOG, and to discuss relevant issues to reduce the computation time.
Journal ArticleDOI
Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey
TL;DR: An extended and updated survey of the recent researches and patents which concern statistical background modeling to achieve a comparative evaluation and to conclude with several promising directions for future research.
Journal ArticleDOI
A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection
TL;DR: This work proposes a spatial coherence variant to background subtraction based on self-organization through artificial neural networks to enhance robustness against false detections and formulate a fuzzy model to deal with decision problems typically arising when crisp settings are involved.
References
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