No dataset too small! Animating 3D motion data to enlarge 2D video databases

Marion Mundt, Henrike Oberlack, Corey Gene Morris, Johannes Funken, Wolfgang Potthast, Jacqueline Alderson

Research output: Chapter in Book/Conference paperConference paperpeer-review

Abstract

This study outlines a technique to leverage the wide availability of high resolution three- dimensional (3D) motion capture data for the purpose of synthesising two-dimensional (2D) video camera views, thereby increasing the availability of 2D video image databases for training machine learning models requiring large datasets. We register 3D marker trajectories to generic 3D body-shapes (hulls) and use a 2D pose estimation algorithm to predict anatomical landmark keypoints in the synthesised 2D video views – a novel approach that addresses the limited data available in elite sport settings. We use 3D long jump data as an exemplar use case and investigate the influence of; 1) varying anthropmetrics, and 2) the 2D camera view, on keypoint estimation accuracy. The results indicated that 2D keypoint determination accuracy is affected by body-shape. Frontal plane camera views result in lower accuracy than sagittal plane camera views.
Original languageEnglish
Title of host publicationISBC Conference 2021
Place of PublicationUSA
PublisherInternational Society of Biomechanics in Sports
Pages25
Number of pages28
Volume39
Edition1
Publication statusPublished - 3 Sep 2021
Event39th Conference of the International Society of Biomechanics in Sport - Virtual, Canberra, Australia
Duration: 3 Sep 20216 Sep 2021

Publication series

NameISBS Proceedings Archive

Conference

Conference39th Conference of the International Society of Biomechanics in Sport
Country/TerritoryAustralia
CityCanberra
Period3/09/216/09/21

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