1. Neuroscience
Download icon

DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning

  1. Jacob M Graving  Is a corresponding author
  2. Daniel Chae
  3. Hemal Naik
  4. Liang Li
  5. Benjamin Koger
  6. Blair R Costelloe
  7. Iain D Couzin
  1. Max Planck Institute of Animal Behavior, Germany
  2. Princeton University, United States
Tools and Resources
  • Cited 16
  • Views 9,152
  • Annotations
Cite this article as: eLife 2019;8:e47994 doi: 10.7554/eLife.47994

Abstract

Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning 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 and robustness. Here we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2× with no loss in accuracy compared to currently-available methods. We demonstrate the versatility of our methods 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.

Article and author information

Author details

  1. Jacob M Graving

    Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
    For correspondence
    jgraving@gmail.com
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5826-467X
  2. Daniel Chae

    Department of Computer Science, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  3. Hemal Naik

    Department for Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
    Competing interests
    No competing interests declared.
  4. Liang Li

    Department for Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
    Competing interests
    No competing interests declared.
  5. Benjamin Koger

    Department for Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
    Competing interests
    No competing interests declared.
  6. Blair R Costelloe

    Department for Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
    Competing interests
    No competing interests declared.
  7. Iain D Couzin

    Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
    Competing interests
    Iain D Couzin, Reviewing editor, eLife.

Funding

National Science Foundation (IOS-1355061)

  • Iain D Couzin

Horizon 2020 Framework Programme (Marie Sklodowska-Curie grant agreement No. 748549)

  • Blair R Costelloe

Nvidia (GPU Grant)

  • Blair R Costelloe

Office of Naval Research (N00014-09-1-1074)

  • Iain D Couzin

Office of Naval Research (N00014-14-1-0635)

  • Iain D Couzin

Army Research Office (W911NG-11-1-0385)

  • Iain D Couzin

Army Research Office (W911NF14-1-0431)

  • Iain D Couzin

Deutsche Forschungsgemeinschaft (DFG Centre of Excellence 2117)

  • Iain D Couzin

University of Konstanz (Zukunftskolleg Investment Grant)

  • Blair R Costelloe

The Strukture-und Innovations fonds fur die Forschung of the State of Baden-Wurttemberg

  • Iain D Couzin

Max Planck Society

  • Iain D Couzin

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All procedures for collecting the zebra (E. grevyi) dataset were reviewed and approved by Ethikrat, the independent Ethics Council of the Max Planck Society. The zebra dataset was collected with the permission of Kenya's National Commission for Science, Technology and Innovation (NACOSTI/P/17/59088/15489 and NACOSTI/P/18/59088/21567) using drones operated by B.R.C. with the permission of the Kenya Civil Aviation Authority (authorization numbers: KCAA/OPS/2117/4 Vol. 2 (80), KCAA/OPS/2117/4 Vol. 2 (81), KCAA/OPS/2117/5 (86) and KCAA/OPS/2117/5 (87); RPAS Operator Certificate numbers: RPA/TP/0005 AND RPA/TP/000-0009).

Reviewing Editor

  1. Josh W Shaevitz, Princeton University, United States

Publication history

  1. Received: April 26, 2019
  2. Accepted: September 18, 2019
  3. Accepted Manuscript published: October 1, 2019 (version 1)
  4. Version of Record published: December 6, 2019 (version 2)

Copyright

© 2019, Graving et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 9,152
    Page views
  • 1,131
    Downloads
  • 16
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Neuroscience
    Laura Gwilliams, Jean-Remi King
    Research Article Updated

    Perception depends on a complex interplay between feedforward and recurrent processing. Yet, while the former has been extensively characterized, the computational organization of the latter remains largely unknown. Here, we use magneto-encephalography to localize, track and decode the feedforward and recurrent processes of reading, as elicited by letters and digits whose level of ambiguity was parametrically manipulated. We first confirm that a feedforward response propagates through the ventral and dorsal pathways within the first 200 ms. The subsequent activity is distributed across temporal, parietal and prefrontal cortices, which sequentially generate five levels of representations culminating in action-specific motor signals. Our decoding analyses reveal that both the content and the timing of these brain responses are best explained by a hierarchy of recurrent neural assemblies, which both maintain and broadcast increasingly rich representations. Together, these results show how recurrent processes generate, over extended time periods, a cascade of decisions that ultimately accounts for subjects’ perceptual reports and reaction times.

    1. Neuroscience
    Vincent Huson et al.
    Research Advance Updated

    Previously, we showed that modulation of the energy barrier for synaptic vesicle fusion boosts release rates supralinearly (Schotten, 2015). Here we show that mouse hippocampal synapses employ this principle to trigger Ca2+-dependent vesicle release and post-tetanic potentiation (PTP). We assess energy barrier changes by fitting release kinetics in response to hypertonic sucrose. Mimicking activation of the C2A domain of the Ca2+-sensor Synaptotagmin-1 (Syt1), by adding a positive charge (Syt1D232N) or increasing its hydrophobicity (Syt14W), lowers the energy barrier. Removing Syt1 or impairing its release inhibitory function (Syt19Pro) increases spontaneous release without affecting the fusion barrier. Both phorbol esters and tetanic stimulation potentiate synaptic strength, and lower the energy barrier equally well in the presence and absence of Syt1. We propose a model where tetanic stimulation activates Syt1-independent mechanisms that lower the energy barrier and act additively with Syt1-dependent mechanisms to produce PTP by exerting multiplicative effects on release rates.