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 contribution

Abstract

Principal component pursuit (PCP) is a state-of-the-art approach for 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 in an incremental manner. We propose a batch-incremental background estimation model using a special weighted low-rank approximation of matrices. Through experiments with real and synthetic video sequences, we demonstrate that our method is superior to the state-of-the-art background estimation algorithms such as GRASTA, ReProCS, incPCP, and GFL.
Original languageEnglish
Title of host publicationIEEE International Conference on Computer Vision (ICCV) Workshops, 2017
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1835-1843
Number of pages10
ISBN (Electronic)978-1-5386-1034-3
ISBN (Print)978-1-5386-1035-0
Publication statusE-pub ahead of print - 22 Oct 2017

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