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OpenMonkeyStudio: Automated Markerless Pose Estimation in Freely Moving Macaques

Praneet C. Bala, Benjamin R. Eisenreich, Seng Bum Michael Yoo, Benjamin Y. Hayden, Hyun Soo Park, Jan Zimmermann
doi: https://doi.org/10.1101/2020.01.31.928861
Praneet C. Bala
Department of Computer Science and Engineering, University of Minnesota, Minneapolis MN 55455
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Benjamin R. Eisenreich
Department of Neuroscience, University of Minnesota, Minneapolis MN 55455
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Seng Bum Michael Yoo
Department of Neuroscience, University of Minnesota, Minneapolis MN 55455
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Benjamin Y. Hayden
Department of Neuroscience, University of Minnesota, Minneapolis MN 55455Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN 55455Center for Neuroengineering, University of Minnesota, Minneapolis MN 55455
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  • For correspondence: benhayden@gmail.com
Hyun Soo Park
Department of Computer Science and Engineering, University of Minnesota, Minneapolis MN 55455
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Jan Zimmermann
Department of Neuroscience, University of Minnesota, Minneapolis MN 55455Center for Magnetic Resonance Research, University of Minnesota, Minneapolis MN 55455Center for Neuroengineering, University of Minnesota, Minneapolis MN 55455
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Abstract

The rhesus macaque is an important model species in several branches of science, including neuroscience, psychology, ethology, and several fields of medicine. The utility of the macaque model would be greatly enhanced by the ability to precisely measure its behavior, specifically, its pose (position of multiple major body landmarks) in freely moving conditions. Existing approaches do not provide sufficient tracking. Here, we describe OpenMonkeyStudio, a novel deep learning-based markerless motion capture system for estimating 3D pose in freely moving macaques in large unconstrained environments. Our system makes use of 62 precisely calibrated and synchronized machine vision cameras that encircle an open 2.45m×2.45m×2.75m enclosure. The resulting multiview image streams allow for novel data augmentation via 3D reconstruction of hand-annotated images that in turn train a robust view-invariant deep neural network model. This view invariance represents an important advance over previous markerless 2D tracking approaches, and allows fully automatic pose inference on unconstrained natural motion. We show that OpenMonkeyStudio can be used to accurately recognize actions and track two monkey social interactions without human intervention. We also make the training data (195,228 images) and trained detection model publicly available.

Footnotes

  • https://github.com/OpenMonkeyStudio

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.
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Posted February 02, 2020.
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OpenMonkeyStudio: Automated Markerless Pose Estimation in Freely Moving Macaques
Praneet C. Bala, Benjamin R. Eisenreich, Seng Bum Michael Yoo, Benjamin Y. Hayden, Hyun Soo Park, Jan Zimmermann
bioRxiv 2020.01.31.928861; doi: https://doi.org/10.1101/2020.01.31.928861
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OpenMonkeyStudio: Automated Markerless Pose Estimation in Freely Moving Macaques
Praneet C. Bala, Benjamin R. Eisenreich, Seng Bum Michael Yoo, Benjamin Y. Hayden, Hyun Soo Park, Jan Zimmermann
bioRxiv 2020.01.31.928861; doi: https://doi.org/10.1101/2020.01.31.928861

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