Online Learning of Linear Predictors for Real-Time Tracking
Abstract
Although fast and reliable, real-time template tracking using linear predictors requires a long training time. The lack of the ability to learn new templates online prevents their use in applications that require fast learning. This especially holds for applications where the scene is not known a priori and multiple templates have to be added online. So far, linear predictors had to be either learned offline [1] or in an iterative manner by starting with a small sized template and growing it over time [2]. In this paper, we propose a fast and simple reformulation of the learning procedure that allows learning new linear predictors online.
Cite
Text
Holzer et al. "Online Learning of Linear Predictors for Real-Time Tracking." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33718-5_34Markdown
[Holzer et al. "Online Learning of Linear Predictors for Real-Time Tracking." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/holzer2012eccv-online/) doi:10.1007/978-3-642-33718-5_34BibTeX
@inproceedings{holzer2012eccv-online,
title = {{Online Learning of Linear Predictors for Real-Time Tracking}},
author = {Holzer, Stefan and Pollefeys, Marc and Ilic, Slobodan and Tan, David Joseph and Navab, Nassir},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {470-483},
doi = {10.1007/978-3-642-33718-5_34},
url = {https://mlanthology.org/eccv/2012/holzer2012eccv-online/}
}