A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives.


Journal

Neuron
ISSN: 1097-4199
Titre abrégé: Neuron
Pays: United States
ID NLM: 8809320

Informations de publication

Date de publication:
14 10 2020
Historique:
received: 03 07 2020
revised: 02 09 2020
accepted: 10 09 2020
entrez: 15 10 2020
pubmed: 16 10 2020
medline: 1 12 2020
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 33058765
pii: S0896-6273(20)30717-0
doi: 10.1016/j.neuron.2020.09.017
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review Video-Audio Media

Langues

eng

Sous-ensembles de citation

IM

Pagination

44-65

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Auteurs

Alexander 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.

Steffen 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.

Jessy 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.

Mackenzie Weygandt 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.

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Classifications MeSH