ML Anthology

Kernel-Based Template Alignment

Igor Guskov
CVPR 2006 pp. 610-617
doi:10.1109/CVPR.2006.162 /cvpr/2006/guskov2006cvpr-kernel/

Abstract

This paper introduces a novel kernel-based method for template tracking in video sequences. The method is derived for a general warping transformation, and its application to affine motion tracking is further explored. Our approach is based on maximization of the multi-kernel Bhattacharyya coefficient with respect to the warp parameters. We explicitly compute the gradient of the similarity functional, and use a quasi-Newton procedure for optimization. Additionally, we consider a simple extension of the method that employs an illumination model correction to allow tracking under varying lighting conditions. The resulting tracking procedure is evaluated on a number of examples including large templates tracking non-rigidly moving textured areas.

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Cite

Text

Guskov. "Kernel-Based Template Alignment." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.162

Markdown

[Guskov. "Kernel-Based Template Alignment." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/guskov2006cvpr-kernel/) doi:10.1109/CVPR.2006.162

BibTeX

@inproceedings{guskov2006cvpr-kernel,
  title     = {{Kernel-Based Template Alignment}},
  author    = {Guskov, Igor},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2006},
  pages     = {610-617},
  doi       = {10.1109/CVPR.2006.162},
  url       = {https://mlanthology.org/cvpr/2006/guskov2006cvpr-kernel/}
}