Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset-Reference-Cited by-同舟云学术

Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset

Published:2017-02 Issue: Volume:23 Page:1-71
ISSN:1574-0137
Container-title:Computer Science Review
language:en
Short-container-title:Computer Science Review

Author:

Bouwmans Thierry,Sobral Andrews^ORCID,Javed Sajid,Jung Soon Ki^ORCID,Zahzah El-Hadi

Publisher

Elsevier BV

Subject

General Computer Science,Theoretical Computer Science

Reference471 articles.

1. Background modeling using mixture of Gaussians for foreground detection — a survey;Bouwmans;RPCS,2008

2. Subspace learning for background modeling: A survey;Bouwmans;RPCS,2009

3. Recent advanced statistical background modeling for foreground detection: A systematic survey;Bouwmans;RPCS,2011

4. Traditional and recent approaches in background modeling for foreground detection: An overview;Bouwmans;Comput. Sci. Rev.,2014

5. Handbook on Background Modeling and Foreground Detection for Video Surveillance;Bouwmans,2015

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