OpenMonkeyChallenge: Dataset and Benchmark Challenges for Pose Estimation of Non-human Primates

Yuan Yao, Praneet Bala, Abhiraj Mohan, Eliza Bliss-Moreau, Kristine Coleman, Sienna M. Freeman, Christopher J. Machado, Jessica Raper, Jan Zimmermann, Benjamin Y. Hayden, Hyun Soo Park

    Research output: Contribution to journalArticlepeer-review

    Abstract

    The ability to automatically estimate the pose of non-human primates as they move through the world is important for several subfields in biology and biomedicine. Inspired by the recent success of computer vision models enabled by benchmark challenges (e.g., object detection), we propose a new benchmark challenge called OpenMonkeyChallenge that facilitates collective community efforts through an annual competition to build generalizable non-human primate pose estimation models. To host the benchmark challenge, we provide a new public dataset consisting of 111,529 annotated (17 body landmarks) photographs of non-human primates in naturalistic contexts obtained from various sources including the Internet, three National Primate Research Centers, and the Minnesota Zoo. Such annotated datasets will be used for the training and testing datasets to develop generalizable models with standardized evaluation metrics. We demonstrate the effectiveness of our dataset quantitatively by comparing it with existing datasets based on seven state-of-the-art pose estimation models.

    Original languageEnglish (US)
    Pages (from-to)243-258
    Number of pages16
    JournalInternational Journal of Computer Vision
    Volume131
    Issue number1
    DOIs
    StatePublished - Jan 2023

    Keywords

    • Behavioral tracking
    • Dataset and benchmark challenge
    • Deep learning
    • Non-human primates

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition
    • Artificial Intelligence

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