Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Fast and robust animal pose estimation

View ORCID ProfileJacob M. Graving, Daniel Chae, Hemal Naik, Liang Li, Benjamin Koger, View ORCID ProfileBlair R. Costelloe, View ORCID ProfileIain D. Couzin
doi: https://doi.org/10.1101/620245
Jacob M. Graving
1Department of Collective Behaviour, Max Planck Institute for Ornithology, 78464 Konstanz, Germany
2Chair of Biodiversity and Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
3Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jacob M. Graving
  • For correspondence: jgraving@gmail.com icouzin@orn.mpg.de
Daniel Chae
4Department of Computer Science, Princeton University, 08544 Princeton, NJ, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hemal Naik
1Department of Collective Behaviour, Max Planck Institute for Ornithology, 78464 Konstanz, Germany
2Chair of Biodiversity and Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
3Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
5Chair for Computer Aided Medical Procedures, Technische Universität München, 80333 Munich, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Liang Li
1Department of Collective Behaviour, Max Planck Institute for Ornithology, 78464 Konstanz, Germany
2Chair of Biodiversity and Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
3Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Benjamin Koger
1Department of Collective Behaviour, Max Planck Institute for Ornithology, 78464 Konstanz, Germany
2Chair of Biodiversity and Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
3Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Blair R. Costelloe
1Department of Collective Behaviour, Max Planck Institute for Ornithology, 78464 Konstanz, Germany
2Chair of Biodiversity and Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
3Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Blair R. Costelloe
Iain D. Couzin
1Department of Collective Behaviour, Max Planck Institute for Ornithology, 78464 Konstanz, Germany
2Chair of Biodiversity and Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
3Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Iain D. Couzin
  • For correspondence: jgraving@gmail.com icouzin@orn.mpg.de
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Quantitative behavioral measurements are important for answering questions across scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning-based methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal’s body parts directly from images or videos. However, currently-available animal pose estimation methods have limitations in speed, robustness, and usability. Here we introduce an open-source software toolkit, DeepPoseKit, that addresses these problems. Using modern desktop hardware, our methods perform real-time measurements at ~30–110-Hz with offline performance >1000-Hz—approximately 2–6× faster than current methods. We achieve these results while only increasing average error <0.5-pixels compared to the most-accurate methods currently available. We demonstrate the versatility of our approach with multiple challenging animal pose estimation tasks in laboratory and field settings—including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted April 27, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Fast and robust animal pose estimation
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Fast and robust animal pose estimation
Jacob M. Graving, Daniel Chae, Hemal Naik, Liang Li, Benjamin Koger, Blair R. Costelloe, Iain D. Couzin
bioRxiv 620245; doi: https://doi.org/10.1101/620245
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Fast and robust animal pose estimation
Jacob M. Graving, Daniel Chae, Hemal Naik, Liang Li, Benjamin Koger, Blair R. Costelloe, Iain D. Couzin
bioRxiv 620245; doi: https://doi.org/10.1101/620245

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Animal Behavior and Cognition
Subject Areas
All Articles
  • Animal Behavior and Cognition (4672)
  • Biochemistry (10338)
  • Bioengineering (7657)
  • Bioinformatics (26297)
  • Biophysics (13500)
  • Cancer Biology (10670)
  • Cell Biology (15412)
  • Clinical Trials (138)
  • Developmental Biology (8487)
  • Ecology (12805)
  • Epidemiology (2067)
  • Evolutionary Biology (16828)
  • Genetics (11382)
  • Genomics (15467)
  • Immunology (10600)
  • Microbiology (25166)
  • Molecular Biology (10204)
  • Neuroscience (54382)
  • Paleontology (399)
  • Pathology (1665)
  • Pharmacology and Toxicology (2889)
  • Physiology (4334)
  • Plant Biology (9235)
  • Scientific Communication and Education (1586)
  • Synthetic Biology (2554)
  • Systems Biology (6773)
  • Zoology (1461)