Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/wacv.2019.00063 | 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) |
Keywords | DocType | Volume |
Computational modeling,Hidden Markov models,Estimation,Principal component analysis,Training,Stochastic processes,Streaming media | Conference | abs/1712.02249 |
ISSN | Citations | PageRank |
2472-6737 | 1 | 0.35 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aritra Dutta | 1 | 9 | 5.60 |
| Peter Richtárik | 2 | 1314 | 84.53 |