Crisp Weighted Support Vector Regression for Robust Single Model Estimation : Application to Object Tracking in Image Sequences
Abstract
Support Vector Regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with outliers and structured outliers in training data sets commonly encountered in computer vision applications. In this paper, we present a weighted version of SVM for regression. The proposed approach introduces an adaptive binary function that allows a dominant model from a degraded training dataset to be extracted. This binary function progressively separates inliers from outliers following a one-against-all decomposition. Experimental tests show the high robustness of the proposed approach against outliers and residual structured outliers. Next, we validate our algorithm for object tracking and for optic flow estimation.
Cite
Text
Dufrenois et al. "Crisp Weighted Support Vector Regression for Robust Single Model Estimation : Application to Object Tracking in Image Sequences." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383181Markdown
[Dufrenois et al. "Crisp Weighted Support Vector Regression for Robust Single Model Estimation : Application to Object Tracking in Image Sequences." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/dufrenois2007cvpr-crisp/) doi:10.1109/CVPR.2007.383181BibTeX
@inproceedings{dufrenois2007cvpr-crisp,
title = {{Crisp Weighted Support Vector Regression for Robust Single Model Estimation : Application to Object Tracking in Image Sequences}},
author = {Dufrenois, Franck and Colliez, Johan and Hamad, Denis},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2007},
doi = {10.1109/CVPR.2007.383181},
url = {https://mlanthology.org/cvpr/2007/dufrenois2007cvpr-crisp/}
}