Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 |
| Herausgeber (Verlag) | IEEE Computer Society |
| Seiten | 3863-3869 |
| Seitenumfang | 7 |
| ISBN (elektronisch) | 9781665448994 |
| ISBN (Print) | 978-1-6654-4900-7 |
| Publikationsstatus | Veröffentlicht - 2021 |
| Veranstaltung | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, USA / Vereinigte Staaten Dauer: 19 Juni 2021 → 25 Juni 2021 |
Publikationsreihe
| Name | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
|---|
Abstract
Vertical jump height is an important tool to measure athletes' lower body power in sports science and medicine. This work improves upon a previously published self-calibrating algorithm, which determines jump height using a single smartphone camera. The algorithm uses the parabolic fall trajectory obtained by tracking a single feature in a high-speed video. Instead of tracking an ArUco marker, which must be attached to the jumping subject, this work uses the OpenPose neural network for human pose estimation in order to calculate an approximation of the body center of mass. Jump heights obtained this way are compared to the reference heights from a motion capture system and to the results of the original work. The result is a trade-off between increased ease-of-use and slightly diminished accuracy of the jump height measurement.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
Ziele für nachhaltige Entwicklung
Zitieren
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- Harvard
- Apa
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- BibTex
- RIS
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021. IEEE Computer Society, 2021. S. 3863-3869 (2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Markerless camera-based vertical jump height measurement using openpose
AU - Webering, Fritz
AU - Blume, Holger
AU - Allaham, Issam
PY - 2021
Y1 - 2021
N2 - Vertical jump height is an important tool to measure athletes' lower body power in sports science and medicine. This work improves upon a previously published self-calibrating algorithm, which determines jump height using a single smartphone camera. The algorithm uses the parabolic fall trajectory obtained by tracking a single feature in a high-speed video. Instead of tracking an ArUco marker, which must be attached to the jumping subject, this work uses the OpenPose neural network for human pose estimation in order to calculate an approximation of the body center of mass. Jump heights obtained this way are compared to the reference heights from a motion capture system and to the results of the original work. The result is a trade-off between increased ease-of-use and slightly diminished accuracy of the jump height measurement.
AB - Vertical jump height is an important tool to measure athletes' lower body power in sports science and medicine. This work improves upon a previously published self-calibrating algorithm, which determines jump height using a single smartphone camera. The algorithm uses the parabolic fall trajectory obtained by tracking a single feature in a high-speed video. Instead of tracking an ArUco marker, which must be attached to the jumping subject, this work uses the OpenPose neural network for human pose estimation in order to calculate an approximation of the body center of mass. Jump heights obtained this way are compared to the reference heights from a motion capture system and to the results of the original work. The result is a trade-off between increased ease-of-use and slightly diminished accuracy of the jump height measurement.
KW - Convolutional neural network
KW - Gravity
KW - Human pose estimation
KW - Parabola
KW - Sports
KW - Vertical jump height
UR - http://www.scopus.com/inward/record.url?scp=85116050828&partnerID=8YFLogxK
U2 - 10.15488/13695
DO - 10.15488/13695
M3 - Conference contribution
AN - SCOPUS:85116050828
SN - 978-1-6654-4900-7
T3 - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
SP - 3863
EP - 3869
BT - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
PB - IEEE Computer Society
T2 - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
Y2 - 19 June 2021 through 25 June 2021
ER -