Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing
Ostrek M, Rhodin H, Fua P, Müller E, Spörri J (2019)
Sensors 19(19): 4323.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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sensors-19-04323-v2.pdf
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Autor*in
Ostrek, Mirela;
Rhodin, HelgeUniBi ;
Fua, Pascal;
Müller, Erich;
Spörri, Jörg
Abstract / Bemerkung
In this study, we compared a monocular computer vision (MCV)-based approach with the golden standard for collecting kinematic data on ski tracks (i.e., video-based stereophotogrammetry) and assessed its deployment readiness for answering applied research questions in the context of alpine skiing. The investigated MCV-based approach predicted the three-dimensional human pose and ski orientation based on the image data from a single camera. The data set used for training and testing the underlying deep nets originated from a field experiment with six competitive alpine skiers. The normalized mean per joint position error of the MVC-based approach was found to be 0.08 ± 0.01 m. Knee flexion showed an accuracy and precision (in parenthesis) of 0.4 ± 7.1° (7.2 ± 1.5°) for the outside leg, and −0.2 ± 5.0° (6.7 ± 1.1°) for the inside leg. For hip flexion, the corresponding values were −0.4 ± 6.1° (4.4° ± 1.5°) and −0.7 ± 4.7° (3.7 ± 1.0°), respectively. The accuracy and precision of skiing-related metrics were revealed to be 0.03 ± 0.01 m (0.01 ± 0.00 m) for relative center of mass position, −0.1 ± 3.8° (3.4 ± 0.9) for lean angle, 0.01 ± 0.03 m (0.02 ± 0.01 m) for center of mass to outside ankle distance, 0.01 ± 0.05 m (0.03 ± 0.01 m) for fore/aft position, and 0.00 ± 0.01 m2 (0.01 ± 0.00 m2) for drag area. Such magnitudes can be considered acceptable for detecting relevant differences in the context of alpine skiing.
Stichworte
biomechanics;
human pose estimation;
markerless tracking;
video-based 3D kinematics;
technical validation;
alpine ski racing
Erscheinungsjahr
2019
Zeitschriftentitel
Sensors
Band
19
Ausgabe
19
Art.-Nr.
4323
Urheberrecht / Lizenzen
eISSN
1424-8220
Page URI
https://pub.uni-bielefeld.de/record/2991924
Zitieren
Ostrek M, Rhodin H, Fua P, Müller E, Spörri J. Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing. Sensors. 2019;19(19): 4323.
Ostrek, M., Rhodin, H., Fua, P., Müller, E., & Spörri, J. (2019). Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing. Sensors, 19(19), 4323. https://doi.org/10.3390/s19194323
Ostrek, Mirela, Rhodin, Helge, Fua, Pascal, Müller, Erich, and Spörri, Jörg. 2019. “Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing”. Sensors 19 (19): 4323.
Ostrek, M., Rhodin, H., Fua, P., Müller, E., and Spörri, J. (2019). Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing. Sensors 19:4323.
Ostrek, M., et al., 2019. Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing. Sensors, 19(19): 4323.
M. Ostrek, et al., “Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing”, Sensors, vol. 19, 2019, : 4323.
Ostrek, M., Rhodin, H., Fua, P., Müller, E., Spörri, J.: Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing. Sensors. 19, : 4323 (2019).
Ostrek, Mirela, Rhodin, Helge, Fua, Pascal, Müller, Erich, and Spörri, Jörg. “Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing”. Sensors 19.19 (2019): 4323.
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