A Batch-Incremental Video Background Estimation Model Using Weighted Low-Rank Approximation of Matrices

Aritra Dutta, Xin Li, Peter Richtarik

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

5 Scopus citations

Abstract

Principal component pursuit (PCP) is a state-of-the-art approach to background estimation problems. Due to their higher computational cost, PCP algorithms, such as robust principal component analysis (RPCA) and its variants, are not feasible in processing high definition videos. To avoid the curse of dimensionality in those algorithms, several methods have been proposed to solve the background estimation problem incrementally. We build a batch-incremental background estimation model by using a special weighted low-rank approximation of matrices. Through experiments with real and synthetic video sequences, we demonstrate that our model is superior to the existing state-of-the-art background estimation algorithms such as GRASTA, ReProCS, incPCP, and GFL.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1835-1843
Number of pages9
ISBN (Electronic)9781538610343
DOIs
StatePublished - Jan 19 2018
Event16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 - Venice, Italy
Duration: Oct 22 2017Oct 29 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Volume2018-January

Conference

Conference16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
CountryItaly
CityVenice
Period10/22/1710/29/17

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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