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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Postprint. © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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| Author: | Katja LudwigGND, Julian LorenzGND, Robin SchönGND, Rainer LienhartGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-1034807 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/103480 |
| Parent Title (English): | 2023 IEEE/CVF International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
| Publisher: | IEEE |
| Place of publication: | Piscataway, NJ |
| Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2023 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2023/04/11 |
| Note: | An updated version of this paper is available on OPUS: https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-1034807. |
| 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 |
| Latest Publications (not yet published in print): | Aktuelle Publikationen (noch nicht gedruckt erschienen) |
| Licence (German): |  Deutsches Urheberrecht |
