Simultaneous Modeling and Tracking (SMAT) of Feature Sets
Abstract
A novel method for the simultaneous modeling and tracking (SMAT) of a feature set during motion sequence is proposed. The method requires no prior information. Instead the a posteriori distribution of appearance and shape is built up incrementally using an exemplar based approach. The resulting model is less optimal than when a priori data is used, but can be built in real-time. Data in any form may be used, provided a distance measure and a means to classify outliers exists. Here, a two tier implementation of SMAT is used: at the feature level, mutual information is used to track image patches; and at the object level, a structure model is built from the feature positions. As experiments demonstrate, the tracker is robust and operates in real-time without requiring prelearned data.
Cite
Text
Dowson and Bowden. "Simultaneous Modeling and Tracking (SMAT) of Feature Sets." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.324Markdown
[Dowson and Bowden. "Simultaneous Modeling and Tracking (SMAT) of Feature Sets." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/dowson2005cvpr-simultaneous/) doi:10.1109/CVPR.2005.324BibTeX
@inproceedings{dowson2005cvpr-simultaneous,
title = {{Simultaneous Modeling and Tracking (SMAT) of Feature Sets}},
author = {Dowson, Nicholas D. H. and Bowden, Richard},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2005},
pages = {99-105},
doi = {10.1109/CVPR.2005.324},
url = {https://mlanthology.org/cvpr/2005/dowson2005cvpr-simultaneous/}
}