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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Metadaten
Author:Katja LudwigGND, Julian LorenzGND, Robin SchönGND, Rainer LienhartGND
URN:urn:nbn:de:bvb:384-opus4-1034807
Frontdoor URLhttps://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
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