Online and Batch Supervised Background Estimation via L1 Regression
Abstract
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.
Cite
Text
Dutta and Richtárik. "Online and Batch Supervised Background Estimation via L1 Regression." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019. doi:10.1109/WACV.2019.00063Markdown
[Dutta and Richtárik. "Online and Batch Supervised Background Estimation via L1 Regression." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019.](https://mlanthology.org/wacv/2019/dutta2019wacv-online/) doi:10.1109/WACV.2019.00063BibTeX
@inproceedings{dutta2019wacv-online,
title = {{Online and Batch Supervised Background Estimation via L1 Regression}},
author = {Dutta, Aritra and Richtárik, Peter},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2019},
pages = {541-550},
doi = {10.1109/WACV.2019.00063},
url = {https://mlanthology.org/wacv/2019/dutta2019wacv-online/}
}