# Online and Batch Supervised Background Estimation via L1 Regression

arXiv: https://arxiv.org/abs/1712.02249 · HTML: https://mlsanity.com/paper/1712.02249/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Towards a Better Theoretical Understanding of Independent Subnetwork Training](https://mlsanity.com/paper/2306.16484/) (2023)
- [A Note on Randomized Kaczmarz Algorithm for Solving Doubly-Noisy Linear Systems](https://mlsanity.com/paper/2308.16904/) (2023)
- [Where Have All the Kaczmarz Iterates Gone?](https://mlsanity.com/paper/2510.08563/) (2025)

## Abstract (arXiv)

We propose a surprisingly simple model for supervised video background estimation. Our model is based on ℓ1 regression. As existing methods for ℓ1 regression do not scale to high-resolution videos, we propose several simple and scalable methods for solving the problem, including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent. We show through extensive experiments that our model and methods match or outperform the state-of-the-art online and batch methods in virtually all quantitative and qualitative measures.