An annotated data set for pose estimation of swimmers

  • In this work we present an annotated data set for two-dimensional pose estimation of swimmers. The data set contains fifteen cycles of swimmers swimming backstroke with more than 1200 annotated video frames. A wide variety of subjects was used to create this data set, ranging from adult to teenage swimmers, both, male and female. For each frame of a cycle, the absolute positions of fourteen points corresponding to human joints were manually labeled. The data set proves to be very challenging with respect to partial occlusions and high amounts of background noise, however, it does not contain any out-of-plane motions that would further complicate the task of full body pose estimation. It thus aims at pose estimation and pose tracking algorithms trying to advance the field of recovering human poses in videos with frequently missing parts and under difficult conditions. We explain in detail the creation of the data set, discuss the difficulties we faced, and finally demonstrate how it isIn this work we present an annotated data set for two-dimensional pose estimation of swimmers. The data set contains fifteen cycles of swimmers swimming backstroke with more than 1200 annotated video frames. A wide variety of subjects was used to create this data set, ranging from adult to teenage swimmers, both, male and female. For each frame of a cycle, the absolute positions of fourteen points corresponding to human joints were manually labeled. The data set proves to be very challenging with respect to partial occlusions and high amounts of background noise, however, it does not contain any out-of-plane motions that would further complicate the task of full body pose estimation. It thus aims at pose estimation and pose tracking algorithms trying to advance the field of recovering human poses in videos with frequently missing parts and under difficult conditions. We explain in detail the creation of the data set, discuss the difficulties we faced, and finally demonstrate how it is used to create a training data set containing normalized cycles for action-specific pose tracking.show moreshow less

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Metadaten
Author:Thomas GreifGND, Rainer LienhartGND
URN:urn:nbn:de:bvb:384-opus4-11288
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/1348
Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2009-18)
Type:Report
Language:English
Publishing Institution:Universität Augsburg
Release Date:2010/01/27
Tag:data set; pose estimation; pose tracking; human motion; swimmers
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Multimedia und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht