Cited by 5,187Human Pose and Action RecognitionVideo Surveillance and Tracking MethodsAdvanced Neural Network Applications

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

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Mathis, Alexander, Mamidanna, Pranav, Cury, Kevin M., Abe, Taiga, Murthy, Venkatesh N., Mathis, Mackenzie Weygandt, Bethge, Matthias·2018·Nature Neuroscience

Abstract

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy. Using a deep learning approach to track user-defined body parts during various behaviors across multiple species, the authors show that their toolbox, called DeepLabCut, can achieve human accuracy with only a few hundred frames of training data.

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Crossref · 10.1038/s41593-018-0209-y

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  • Recent developments in behavioral analysis permit sophisticated examination of behavioral phenotypes based on unsupervised machine learning.

    “Finally, recent developments in behavioral analysis [25][26][27] permit sophisticated examination of behavioral phenotypes based on unsupervised machine learning.”

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Authors
Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M.
Affiliations
  • Harvard University (US)
  • University of Tübingen (DE)
  • Mortimer B. Zuckerman Mind Brain Behavior Institute (US)
  • Baylor College of Medicine (US)
Corresponding author
Mathis, Mackenzie Weygandt
Venue
Nature Neuroscience
Published
2018-08-20
Volume
21
Issue
9
Pages
1281-1289
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
OpenAlex
W2887114371
Cited by
5,187
References
51
Citations by year
2026: 875 · 2025: 1081 · 2024: 927 · 2023: 770 · 2022: 619 · 2021: 501 · 2020: 275 · 2019: 118 · 2018: 15
FWCI
113.66
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Neuroscience (publishedVersion)
  • PubMed (publishedVersion)
  • Max Planck Digital Library (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
markerless pose estimation, body part tracking, DeepLabCut, transfer learning, deep neural network, animal behavior quantification, video tracking, limited training data, motor control
MeSH terms
Deep Learning, Algorithms, Animals, Behavior, Behavior, Animal, Drosophila melanogaster, Humans, Male, Mice, Inbred C57BL, Nerve Net, Odorants, Posture, Psychomotor Performance, Transfer, Psychology, Video Recording, Neural Networks, Computer, Mice

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