Multiple-Hand-Gesture Tracking Using Multiple Cameras
Abstract
We propose a method of tracking 3D position, posture, and shapes of human hands from multiple-viewpoint images. Self-occlusion and hand-hand occlusion are serious problems in the vision-based hand tracking. Our system employs multiple-viewpoint and viewpoint selection mechanism to reduce these problems. Each hand position is tracked with a Kalman filler and the motion vectors are updated with image features in selected images that do not include hand-hand occlusion. 3D hand postures are estimated with a small number of reliable image features. These features are extracted based on distance transformation, and they are robust against changes in hand shape and self-occlusion. Finally, a "best view" image is selected for each hand for shape recognition. The shape recognition process is based on a Fourier descriptor. Our system can be used as a user interface device an a virtual environment, replacing glove-type devices and overcoming most of the disadvantages of contact-type devices.
Cite
Text
Utsumi and Ohya. "Multiple-Hand-Gesture Tracking Using Multiple Cameras." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.786980Markdown
[Utsumi and Ohya. "Multiple-Hand-Gesture Tracking Using Multiple Cameras." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/utsumi1999cvpr-multiple/) doi:10.1109/CVPR.1999.786980BibTeX
@inproceedings{utsumi1999cvpr-multiple,
title = {{Multiple-Hand-Gesture Tracking Using Multiple Cameras}},
author = {Utsumi, Akira and Ohya, Jun},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1999},
pages = {1473-1478},
doi = {10.1109/CVPR.1999.786980},
url = {https://mlanthology.org/cvpr/1999/utsumi1999cvpr-multiple/}
}