Rapid Selection of Reliable Templates for Visual Tracking
Abstract
We propose a method that rates the suitability of given templates for template-based tracking in real-time. This is important for applications with online template selection, such as SLAM, where it is essential to track a low number of preferably reliable templates. Our approach is based on simple image features specifically designed to identify texture properties which are problematic for tracking. During a training step, a support vector régresser is learned. It uses a tracking quality measure which considers both convergence rate and speed obtained by simulation of many tracking attempts. Finally, a minimum set of image features is identified to speedup the online selection process. In experiments on real-world video sequences our method improved the detection rate of an existing tracking-by-detection system by 8% on average.
Cite
Text
Alt et al. "Rapid Selection of Reliable Templates for Visual Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010. doi:10.1109/CVPR.2010.5539812Markdown
[Alt et al. "Rapid Selection of Reliable Templates for Visual Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010.](https://mlanthology.org/cvpr/2010/alt2010cvpr-rapid/) doi:10.1109/CVPR.2010.5539812BibTeX
@inproceedings{alt2010cvpr-rapid,
title = {{Rapid Selection of Reliable Templates for Visual Tracking}},
author = {Alt, Nicolas and Hinterstoisser, Stefan and Navab, Nassir},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2010},
pages = {1355-1362},
doi = {10.1109/CVPR.2010.5539812},
url = {https://mlanthology.org/cvpr/2010/alt2010cvpr-rapid/}
}