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REP3D: 3D Human Motion Capture Dataset for Athletic Movement

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2017-12-12
Authors/Contributors
Abstract
The field of human 3D pose estimation suffers from a small population of diverse public motion capture datasets, each with a low number of environments and subjects. We propose a new dataset including 45 participants and 22 environments, using motion capture technology that allows data collection in arbitrary locations. The dataset is composed of video and motion capture data for athletic actions selected from golf and baseball, recorded from a plurality of angles and distances. The annotation process for semi-automatically aligning video data with ground truth 3D joint locations is fully outlined. The performance of a modern human 3D pose estimation model on a subset of the dataset is reported.
Document
Identifier
etd10468
Copyright statement
Copyright is held by the author.
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Mori, Greg
Member of collection
Download file Size
etd10468_JSmith.pdf 15.18 MB

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