Online and Batch Supervised Background Estimation Via L1 Regression

Aritra Dutta, Peter Richtarik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


We propose a surprisingly simple model to estimate supervised video backgrounds. Our model is based on L1 regression. As existing methods for L1 regression do not scale to high-resolution videos, we propose several simple, fast, and scalable methods including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent to solve the problem. Our extensive implementations of the model and methods show that they match or outperform other state-of-the-art online and batch methods that are both supervised and unsupervised in virtually all quantitative and qualitative measures and in fractions of their execution time.
Original languageEnglish (US)
Title of host publication2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Print)9781728119755
StatePublished - Mar 8 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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