Online Multiple Classifier Boosting for Object Tracking
Abstract
This paper presents a new online multi-classifier boosting algorithm for learning object appearance models. In many cases the appearance model is multi-modal, which we capture by training and updating multiple strong classifiers. The proposed algorithm jointly learns the classifiers and a soft partitioning of the input space, defining an area of expertise for each classifier. We show how this formulation improves the specificity of the strong classifiers, allowing simultaneous location and pose estimation in a tracking task. The proposed online scheme iteratively adapts the classifiers during tracking. Experiments show that the algorithm successfully learns multi-modal appearance models during a short initial training phase, subsequently updating them for tracking an object under rapid appearance changes.
Cite
Text
Kim et al. "Online Multiple Classifier Boosting for Object Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010. doi:10.1109/CVPRW.2010.5543889Markdown
[Kim et al. "Online Multiple Classifier Boosting for Object Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010.](https://mlanthology.org/cvprw/2010/kim2010cvprw-online/) doi:10.1109/CVPRW.2010.5543889BibTeX
@inproceedings{kim2010cvprw-online,
title = {{Online Multiple Classifier Boosting for Object Tracking}},
author = {Kim, Tae-Kyun and Woodley, Thomas and Stenger, Björn and Cipolla, Roberto},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2010},
pages = {1-6},
doi = {10.1109/CVPRW.2010.5543889},
url = {https://mlanthology.org/cvprw/2010/kim2010cvprw-online/}
}