A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives.
- Mathis A Center for Neuroprosthetics, Center for Intelligent Systems, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; The Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA. Electronic address: alexander.mathis@epfl.ch.
- Schneider S The Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA; University of Tübingen and International Max Planck Research School for Intelligent Systems, Tübingen, Germany.
- Lauer J Center for Neuroprosthetics, Center for Intelligent Systems, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; The Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA.
- Mathis MW Center for Neuroprosthetics, Center for Intelligent Systems, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; The Rowland Institute at Harvard, Harvard University, Cambridge, MA, USA. Electronic address: mackenzie.mathis@epfl.ch.
- 2020-10-15
Published in:
- Neuron. - 2020
Algorithms
Animals
Deep Learning
Humans
Motion
Motor Activity
Movement
Neural Networks, Computer
Video Recording
English
Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. Recent advances in deep learning have tremendously advanced our ability to predict posture directly from videos, which has quickly impacted neuroscience and biology more broadly. In this primer, we review the budding field of motion capture with deep learning. In particular, we will discuss the principles of those novel algorithms, highlight their potential as well as pitfalls for experimentalists, and provide a glimpse into the future.
- Language
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- English
- Open access status
- bronze
- Identifiers
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- DOI 10.1016/j.neuron.2020.09.017
- PMID 33058765
- Persistent URL
- https://folia.unifr.ch/global/documents/69772
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