Tracking Objects Using Density Matching and Shape Priors
Abstract
We present a novel method for tracking objects by combining density matching with shape priors. Density matching is a tracking method which operates by maximizing the Bhattacharyya similarity measure between the photometric distribution from an estimated image region and a model photometric distribution. Such trackers can be expressed as PDE-based curve evolutions, which can be implemented using level sets. Shape priors can be combined with this levelset implementation of density matching by representing the shape priors as a series of level sets; a variational approach allows for a natural, parametrization-independent shape term to be derived. Experimental results on real image sequences are shown.
Cite
Text
Zhang and Freedman. "Tracking Objects Using Density Matching and Shape Priors." IEEE/CVF International Conference on Computer Vision, 2003. doi:10.1109/ICCV.2003.1238466Markdown
[Zhang and Freedman. "Tracking Objects Using Density Matching and Shape Priors." IEEE/CVF International Conference on Computer Vision, 2003.](https://mlanthology.org/iccv/2003/zhang2003iccv-tracking/) doi:10.1109/ICCV.2003.1238466BibTeX
@inproceedings{zhang2003iccv-tracking,
title = {{Tracking Objects Using Density Matching and Shape Priors}},
author = {Zhang, Tao and Freedman, Daniel},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2003},
pages = {1056-1062},
doi = {10.1109/ICCV.2003.1238466},
url = {https://mlanthology.org/iccv/2003/zhang2003iccv-tracking/}
}