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Artificial Intelligence Review
Erschienen in: Artificial Intelligence Review 2/2018

30.01.2017

Review of background subtraction methods using Gaussian mixture model for video surveillance systems

verfasst von: Kalpana Goyal, Jyoti Singhai

Erschienen in: Artificial Intelligence Review | Ausgabe 2/2018

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Abstract

Foreground detection or moving object detection is a fundamental and critical task in video surveillance systems. Background subtraction using Gaussian Mixture Model (GMM) is a widely used approach for foreground detection. Many improvements have been proposed over the original GMM developed by Stauffer and Grimson (IEEE Computer Society conference on computer vision and pattern recognition, vol 2, Los Alamitos, pp 246–252, 1999. doi:10.​1109/​CVPR.​1999.​784637) to accommodate various challenges experienced in video surveillance systems. This paper presents a review of various background subtraction algorithms based on GMM and compares them on the basis of quantitative evaluation metrics. Their performance analysis is also presented to determine the most appropriate background subtraction algorithm for the specific application or scenario of video surveillance systems.
Vorheriger Artikel An empirical evaluation of hierarchical feature selection methods for classification in bioinformatics datasets with gene ontology-based features
Nächster Artikel A multi-objective genetic algorithm for simultaneous model and feature selection for support vector machines

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Metadaten
Titel
Review of background subtraction methods using Gaussian mixture model for video surveillance systems
verfasst von
Kalpana Goyal
Jyoti Singhai
Publikationsdatum
30.01.2017
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 2/2018
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-017-9542-x

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