A Subset Approach to Contour Tracking in Clutter
Abstract
A new method for tracking contours of moving objects in clutter is presented. For a given object, a model of its contours is learned from training data in the form of a subset of contour space. Greater complexity is added to the contour model by analyzing rigid and non-rigid transformations of contours separately. In the course of tracking, multiple contours may be observed due to the presence of extraneous edges in the form of clutter; the learned model guides the algorithm in picking out the correct one. The algorithm, which is posed as a solution to a minimization problem, is made efficient by the use of several iterative schemes. Results applying the proposed algorithm to the tracking of a flexing finger and to a conversing individual's lips are presented.
Cite
Text
Freedman and Brandstein. "A Subset Approach to Contour Tracking in Clutter." IEEE/CVF International Conference on Computer Vision, 1999. doi:10.1109/ICCV.1999.791226Markdown
[Freedman and Brandstein. "A Subset Approach to Contour Tracking in Clutter." IEEE/CVF International Conference on Computer Vision, 1999.](https://mlanthology.org/iccv/1999/freedman1999iccv-subset/) doi:10.1109/ICCV.1999.791226BibTeX
@inproceedings{freedman1999iccv-subset,
title = {{A Subset Approach to Contour Tracking in Clutter}},
author = {Freedman, Daniel and Brandstein, Michael S.},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1999},
pages = {242-247},
doi = {10.1109/ICCV.1999.791226},
url = {https://mlanthology.org/iccv/1999/freedman1999iccv-subset/}
}