Literature DB >> 33584214

MacaquePose: A Novel "In the Wild" Macaque Monkey Pose Dataset for Markerless Motion Capture.

Rollyn Labuguen1, Jumpei Matsumoto2, Salvador Blanco Negrete1, Hiroshi Nishimaru2, Hisao Nishijo2, Masahiko Takada3, Yasuhiro Go4,5, Ken-Ichi Inoue3, Tomohiro Shibata1.   

Abstract

Video-based markerless motion capture permits quantification of an animal's pose and motion, with a high spatiotemporal resolution in a naturalistic context, and is a powerful tool for analyzing the relationship between the animal's behaviors and its brain functions. Macaque monkeys are excellent non-human primate models, especially for studying neuroscience. Due to the lack of a dataset allowing training of a deep neural network for the macaque's markerless motion capture in the naturalistic context, it has been challenging to apply this technology for macaques-based studies. In this study, we created MacaquePose, a novel open dataset with manually labeled body part positions (keypoints) for macaques in naturalistic scenes, consisting of >13,000 images. We also validated the application of the dataset by training and evaluating an artificial neural network with the dataset. The results indicated that the keypoint estimation performance of the trained network was close to that of a human-level. The dataset will be instrumental to train/test the neural networks for markerless motion capture of the macaques and developments of the algorithms for the networks, contributing establishment of an innovative platform for behavior analysis for non-human primates for neuroscience and medicine, as well as other fields using macaques as a model organism.
Copyright © 2021 Labuguen, Matsumoto, Negrete, Nishimaru, Nishijo, Takada, Go, Inoue and Shibata.

Entities:  

Keywords:  behavior analysis; deep learning; large-scale dataset; non-human primate; pose estimation

Year:  2021        PMID: 33584214      PMCID: PMC7874091          DOI: 10.3389/fnbeh.2020.581154

Source DB:  PubMed          Journal:  Front Behav Neurosci        ISSN: 1662-5153            Impact factor:   3.558


  22 in total

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  6 in total

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Review 2.  Automated pose estimation in primates.

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  6 in total

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