ArticleInternational journal of computer vision2023
OpenMonkeyChallenge: Dataset and Benchmark Challenges for Pose Estimation of Non-human Primates.
Article in International journal of computer vision, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Realistic monkey body animation reveals an uncanny valley in macaque body perception.PLoS biology · 2026Article
- A framework for quantifying the mechanics of dexterous grasp.bioRxiv : the preprint server for biology · 2026Article
- Three-dimensional markerless motion capture of multiple freely behaving monkeys toward automated characterization of social behavior.Science advances · 2025Article
- Computer vision for primate behavior analysis in the wild.Nature methods · 2025Review
- InteBOMB: Integrating generic object tracking and segmentation with pose estimation for animal behavior analysis.Zoological research · 2025Article
- From Forest to Zoo: Great Ape Behavior Recognition with ChimpBehave.International journal of computer vision · 2025Article
- Ethological computational psychiatry: Challenges and opportunities.Current opinion in neurobiology · 2024Review
- Article
- Automated pose estimation in primates.American journal of primatology · 2022Review
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Authors and funding
11 authors.
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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.
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Registered trials
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