Article
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This version is not peer-reviewed
Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks
Version 1
: Received: 2 August 2021 / Approved: 3 August 2021 / Online: 3 August 2021 (11:52:46 CEST)
A peer-reviewed article of this Preprint also exists.
Skurowski, P.; Pawlyta, M. Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks. Sensors 2021, 21, 6115. Skurowski, P.; Pawlyta, M. Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks. Sensors 2021, 21, 6115.
Abstract
Optical motion capture is a mature contemporary technique for the acquisition of motion data, alas it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings. The article reviews various neural network architectures applied for gap filling problem in motion capture sequences within FBM framework providing the representation for body kinematic structure. The results are compared with interpolation and matrix completion methods. We found out, that for longer sequences simple linear feedforward neural networks can outperform the other, sophisticated architectures. We were also able to identify, that acceleration and monotonicity of input sequence are the parameters that have a notable impact on the obtained results.
Keywords
motion capture; neural networks; reconstruction; gap filling; FFNN; LSTM; BILSTM; GRU
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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