All keypoints you need: detecting arbitrary keypoints on the body of triple, high, and long jump athletes
- Performance analyses based on videos are commonly used by coaches of athletes in various sports disciplines. In individual sports, these analyses mainly comprise the body posture. This paper focuses on the disciplines of triple, high, and long jump, which require fine-grained locations of the athlete’s body. Typical human pose estimation datasets provide only a very limited set of keypoints, which is not sufficient in this case. Therefore, we propose a method to detect arbitrary keypoints on the whole body of the athlete by leveraging the limited set of annotated keypoints and auto- generated segmentation masks of body parts. Evaluations show that our model is capable of detecting keypoints on the head, torso, hands, feet, arms, and legs, including also bent elbows and knees. We analyze and compare different techniques to encode desired keypoints as the model’s input and their embedding for the Transformer backbone.
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| Author: | Katja LudwigGND, Julian LorenzGND, Robin SchönORCiDGND, Rainer LienhartORCiDGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-1034807 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/103480 |
| ISBN: | 979-8-3503-0249-3OPAC |
| Parent Title (English): | 2023 IEEE/CVF International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 17 2023 to June 24 2023, Vancouver, BC, Canada |
| Publisher: | IEEE |
| Place of publication: | Piscataway, NJ |
| Type: | Conference Proceeding |
| Language: | English |
| Date of Publication (online): | 2023/04/11 |
| Year of first Publication: | 2023 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2023/04/11 |
| First Page: | 5179 |
| Last Page: | 5187 |
| Note: | An updated version of this paper is available on OPUS: https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-1034807. |
| DOI: | https://doi.org/10.1109/CVPRW59228.2023.00546 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Licence (German): |  Deutsches Urheberrecht |
