Human Spine Motion Capture Using Perforated Kinesiology Tape
Abstract
In this work, we present a marker-based multi-view spine tracking method that is specifically adjusted to the requirements for movements in sports. A maximal focus is on the accurate detection of markers and fast usage of the system. For this task, we take advantage of the prior knowledge of the arrangement of dots in perforated kinesiology tape. We detect the tape and its dots using a Mask R-CNN and a blob detector. Here, we can focus on detection only while skipping any image-based feature encoding or matching. We conduct a reasoning in 3D by a linear program and Markov random fields, in which the structure of the kinesiology tape is modeled and the shape of the spine is optimized. In comparison to state-of-the-art systems, we demonstrate that our system achieves high precision and marker density, is robust against occlusions, and capable of capturing fast movements.
Cite
Text
Hachmann and Rosenhahn. "Human Spine Motion Capture Using Perforated Kinesiology Tape." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00543Markdown
[Hachmann and Rosenhahn. "Human Spine Motion Capture Using Perforated Kinesiology Tape." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/hachmann2023cvprw-human/) doi:10.1109/CVPRW59228.2023.00543BibTeX
@inproceedings{hachmann2023cvprw-human,
title = {{Human Spine Motion Capture Using Perforated Kinesiology Tape}},
author = {Hachmann, Hendrik and Rosenhahn, Bodo},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2023},
pages = {5149-5157},
doi = {10.1109/CVPRW59228.2023.00543},
url = {https://mlanthology.org/cvprw/2023/hachmann2023cvprw-human/}
}