Hierarchical Particle Filtering for 3D Hand Tracking
Abstract
We present a fast and accurate 3D hand tracking method which relies on RGB-D data. The method follows a model based approach using a hierarchical particle filter variant to track the model's state. The filter estimates the probability density function of the state's posterior. As such, it has increased robustness to observation noise and compares favourably to existing methods that can be trapped in local minima resulting in track loses. The data likelihood term is calculated by measuring the discrepancy between the rendered 3D model and the observations. Extensive experiments with real and simulated data show that hand tracking is achieved at a frame rate of 90fps with less that 10mm average error using a GPU implementation, thus comparing favourably to the state of the art in terms of both speed and tracking accuracy.
Cite
Text
Makris et al. "Hierarchical Particle Filtering for 3D Hand Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015. doi:10.1109/CVPRW.2015.7301343Markdown
[Makris et al. "Hierarchical Particle Filtering for 3D Hand Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015.](https://mlanthology.org/cvprw/2015/makris2015cvprw-hierarchical/) doi:10.1109/CVPRW.2015.7301343BibTeX
@inproceedings{makris2015cvprw-hierarchical,
title = {{Hierarchical Particle Filtering for 3D Hand Tracking}},
author = {Makris, Alexandros and Kyriazis, Nikolaos and Argyros, Antonis A.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2015},
pages = {8-17},
doi = {10.1109/CVPRW.2015.7301343},
url = {https://mlanthology.org/cvprw/2015/makris2015cvprw-hierarchical/}
}