Physiological Modelling for Improved Reliability in Silhouette-Driven Gradient-Based Hand Tracking
Abstract
We present a gradient-based motion capture system that robustly tracks a human hand, based on abstracted visual information - silhouettes. Despite the ambiguity in the visual data and despite the vulnerability of gradient-based methods in the face of such ambiguity, we minimise problems related to misfit by using a model of the hand's physiology, which is entirely non-visual, subject-invariant, and assumed to be known a priori. By modelling seven distinct aspects of the hand's physiology we derive prior densities which are incorporated into the tracking system within a Bayesian framework. We demonstrate how the posterior is formed, and how our formulation leads to the extraction of the maximum a posteriori estimate using a gradient-based search. Our results demonstrate an enormous improvement in tracking precision and reliability, while also achieving near real-time performance.
Cite
Text
Kaimakis and Lasenby. "Physiological Modelling for Improved Reliability in Silhouette-Driven Gradient-Based Hand Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009. doi:10.1109/CVPRW.2009.5204252Markdown
[Kaimakis and Lasenby. "Physiological Modelling for Improved Reliability in Silhouette-Driven Gradient-Based Hand Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009.](https://mlanthology.org/cvprw/2009/kaimakis2009cvprw-physiological/) doi:10.1109/CVPRW.2009.5204252BibTeX
@inproceedings{kaimakis2009cvprw-physiological,
title = {{Physiological Modelling for Improved Reliability in Silhouette-Driven Gradient-Based Hand Tracking}},
author = {Kaimakis, Paris and Lasenby, Joan},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2009},
pages = {19-26},
doi = {10.1109/CVPRW.2009.5204252},
url = {https://mlanthology.org/cvprw/2009/kaimakis2009cvprw-physiological/}
}