Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
| Subtitle of host publication | CVPRW 2023 |
| Publisher | IEEE Computer Society |
| Pages | 5149-5157 |
| Number of pages | 9 |
| ISBN (electronic) | 9798350302493 |
| Publication status | Published - 2023 |
| Event | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 - Vancouver, Canada Duration: 17 Jun 2023 → 24 Jun 2023 |
Publication series
| Name | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
|---|---|
| Volume | 2023-June |
| ISSN (Print) | 2160-7508 |
| ISSN (electronic) | 2160-7516 |
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.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Engineering(all)
- Electrical and Electronic Engineering
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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: CVPRW 2023. IEEE Computer Society, 2023. p. 5149-5157 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2023-June).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Human Spine Motion Capture using Perforated Kinesiology Tape
AU - Hachmann, Hendrik
AU - Rosenhahn, Bodo
N1 - Funding Information: This work was supported by the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor (grant no. 01DD20003) and the AI service center KISSKI (grant no. 01IS22093C), the Center for Digital Innovations (ZDIN) and the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122). The authors also thank Dr. Oliver Müller for fruitful discussions and hints.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85170829141&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2306.02930
DO - 10.48550/arXiv.2306.02930
M3 - Conference contribution
AN - SCOPUS:85170829141
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 5149
EP - 5157
BT - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
PB - IEEE Computer Society
T2 - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
Y2 - 17 June 2023 through 24 June 2023
ER -