Cited by 450Human Pose and Action RecognitionAdvanced Neural Network ApplicationsSmart Agriculture and AI

Deep learning tools for the measurement of animal behavior in neuroscience

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Mathis, Mackenzie Weygandt, Mathis, Alexander·2020·Current Opinion in Neurobiology

Abstract

Recent advances in computer vision have made accurate, fast and robust measurement of animal behavior a reality. In the past years powerful tools specifically designed to aid the measurement of behavior have come to fruition. Here we discuss how capturing the postures of animals-pose estimation - has been rapidly advancing with new deep learning methods. While challenges still remain, we envision that the fast-paced development of new deep learning tools will rapidly change the landscape of realizable real-world neuroscience.

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Crossref · 10.1016/j.conb.2019.10.008

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98 of 98 references verified0 citation errors · 0 not checkable

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  • Marker-less DL methods for pose estimation have tremendously improved over the last years and already impacted biology.

    “For pose estimation, marker-less methods based on DL have tremendously improved over the last years and already impacted biology105.”

    From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [105]

  • Modern ML has rapidly changed laboratory-based non-invasive observation of animals.

    “In recent years, modern ML has rapidly changed laboratory-based non-invasive observation of animals76,105.”

    From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [105]

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Authors
Mathis, M. W., & Mathis, A.
Affiliations
Harvard University (US)
Corresponding author
Mathis, Mackenzie Weygandt
Venue
Current Opinion in Neurobiology
Publisher
Elsevier BV
Published
2020-02
Volume
60
Pages
1-11
ISSN
0959-4388 (ISSN-L)
OpenAlex
W2990943996
Cited by
450
References
98
Citations by year
2026: 47 · 2025: 80 · 2024: 65 · 2023: 83 · 2022: 74 · 2021: 61 · 2020: 40
FWCI
4.64
Type
journal article
Language
en
Open access
Locations
  • Current Opinion in Neurobiology (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Funders
Rowland Institute at Harvard
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Keywords
pose estimation, deep learning, computer vision
MeSH terms
Deep Learning, Animals, Behavior, Animal, Neurosciences

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