Swimmer detection and pose estimation for continuous stroke-rate determination

  • In this work we propose a novel approach to automatically detect a swimmer and estimate his/her pose continuously in order to derive an estimate of his/her stroke rate given that we observe the swimmer from the side. We divide a swimming cycle of each stroke into several intervals. Each interval represents a pose of the stroke. We use specifically trained object detectors to detect each pose of a stroke within a video and count the number of occurrences per time unit of the most distinctive poses (so-called key poses) of a stroke to continuously infer the stroke rate. We extensively evaluate the overall performance and the influence of the selected poses for all swimming styles on a data set consisting of a variety of swimmers.

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Metadaten
Author:Dan ZechaGND, Thomas GreifGND, Rainer LienhartORCiDGND
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/117490
ISSN:0277-786XOPAC
Parent Title (English):Multimedia on Mobile Devices 2012; and Multimedia Content Access: Algorithms and Systems VI - IS&T/SPIE Electronic Imaging, 22-26 January 2012, Burlingame, California, USA
Publisher:SPIE
Place of publication:Bellingham, WA
Editor:Cees G. M. Snoek, Nicu Sebe, Lyndon Kennedy, Reiner Creutzburg, David Akopian
Type:Conference Proceeding
Language:English
Year of first Publication:2012
Release Date:2024/12/12
First Page:830410
Series:Proceedings of SPIE ; 8304
DOI:https://doi.org/10.1117/12.908309
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
Nachhaltigkeitsziele
Nachhaltigkeitsziele / Ziel 3 - Gesundheit und Wohlergehen

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