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2019 | OriginalPaper | Chapter

Auto-labelling of Markers in Optical Motion Capture by Permutation Learning

Authors : Saeed Ghorbani, Ali Etemad, Nikolaus F. Troje

Published in: Advances in Computer Graphics

Publisher: Springer International Publishing

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Abstract

Optical marker-based motion capture is a vital tool in applications such as motion and behavioural analysis, animation, and biomechanics. Labelling, that is, assigning optical markers to the pre-defined positions on the body, is a time consuming and labour intensive post-processing part of current motion capture pipelines. The problem can be considered as a ranking process in which markers shuffled by an unknown permutation matrix are sorted to recover the correct order. In this paper, we present a framework for automatic marker labelling which first estimates a permutation matrix for each individual frame using a differentiable permutation learning model and then utilizes temporal consistency to identify and correct remaining labelling errors. Experiments conducted on the test data show the effectiveness of our framework.

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Title: Auto-labelling of Markers in Optical Motion Capture by Permutation Learning
Authors: Saeed Ghorbani
Ali Etemad
Nikolaus F. Troje
Publisher: Springer International Publishing
Book: Advances in Computer Graphics
Print ISBN: 978-3-030-22513-1

Electronic ISBN: 978-3-030-22514-8

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-22514-8_14

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