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A neural network ap...
A neural network approach to missing marker reconstruction in human motion capture
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- Kucherenko, Taras, 1994- (författare)
- KTH,Robotik, perception och lärande, RPL
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- Beskow, Jonas (författare)
- KTH,Tal, musik och hörsel, TMH
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- Kjellström, Hedvig, 1973- (författare)
- KTH,Robotik, perception och lärande, RPL
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(creator_code:org_t)
- 2018
- Engelska.
- Relaterad länk:
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https://kth.diva-por... (primary) (Raw object)
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visa fler...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Optical motion capture systems have become a widely used technology in various fields, such as augmented reality, robotics, movie production, etc. Such systems use a large number of cameras to triangulate the position of optical markers.The marker positions are estimated with high accuracy. However, especially when tracking articulated bodies, a fraction of the markers in each timestep is missing from the reconstruction. In this paper, we propose to use a neural network approach to learn how human motion is temporally and spatially correlated, and reconstruct missing markers positions through this model. We experiment with two different models, one LSTM-based and one time-window-based. Both methods produce state-of-the-art results, while working online, as opposed to most of the alternative methods, which require the complete sequence to be known. The implementation is publicly available at https://github.com/Svito-zar/NN-for-Missing-Marker-Reconstruction .
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Människa-datorinteraktion (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Human Computer Interaction (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- missing markers
- reconstruction
- neural network
- deep learning
- Computer Science
- Datalogi
Publikations- och innehållstyp
- vet (ämneskategori)
- ovr (ämneskategori)