Change detection for video sequences based on incremental subspace learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a novel methodology for change detection in video sequences, which consists in the use of projection of the first eigenvector over the current frame in the video sequence. These eigenvectors are computed using the Incremental Principal Component Analysis (IPCA), assuming that the incremental computation of the eigenvalues and eigenvectors is made using the incremental block approach considering only two frames i.e. the past and the current frames in each incremental block. The main contribution of this work, is the use of the idea that the first eigenvector projects the maximum variability in their data matrix and then by using the incremental block of two frames in the IPCA, the maximum variability in those images could be considered as the change between them; such that after the post-processing in the projected matrix, we are able to labeled the change between the past and the current frames.

Original languageEnglish
Title of host publicationNew Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 17th International Conference, SoMeT 2018
EditorsHamido Fujita, Enrique Herrera-Viedma
PublisherIOS Press BV
Pages71-84
Number of pages14
ISBN (Electronic)9781614998990
DOIs
StatePublished - 2018
Event17th International Conference on New Trends in Intelligent Software Methodology Tools and Techniques, SoMeT 2018 - Granada, Spain
Duration: 26 Sep 201828 Sep 2018

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume303
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference17th International Conference on New Trends in Intelligent Software Methodology Tools and Techniques, SoMeT 2018
Country/TerritorySpain
CityGranada
Period26/09/1828/09/18

Keywords

  • Change detection
  • IPCA
  • Motion detection
  • Subspace learning

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