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

OpenMonkeyChallenge: Dataset and Benchmark Challenges for Pose Estimation of Non-human Primates.
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DOI:
10.1007/s11263-022-01698-2
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发表时间:
2023-01
影响因子:
19.5
通讯作者:
Park, Hyun Soo
Park, Hyun Soo
中科院分区:
计算机科学2区
文献类型:
--
作者:
Yao, Yuan;Bala, Praneet;Mohan, Abhiraj;Bliss-Moreau, Eliza;Coleman, Kristine;Freeman, Sienna M.;Machado, Christopher J.;Raper, Jessica;Zimmermann, Jan;Hayden, Benjamin Y.;Park, Hyun Soo

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自动估计非人类灵长类动物在世界上移动时的姿态的能力对生物学和生物医学的几个子领域很重要。受到最近由基准挑战(例如,目标检测),我们提出了一个新的基准挑战,称为OpenMonkeyChallenge,通过每年的竞争,以促进集体社区的努力,以建立可推广的非人类灵长类动物姿态估计模型。为了主持基准挑战,我们提供了一个新的公共数据集,由111,529张注释(17个身体地标)照片组成,这些照片是从各种来源(包括互联网,三个国家灵长类动物研究中心和明尼苏达动物园)获得的自然环境中的非人类灵长类动物。这些附加说明的数据集将用于培训和测试数据集,以开发具有标准化评价指标的可推广模型。我们证明了我们的数据集的有效性定量比较它与现有的数据集的基础上七个国家的最先进的姿态估计模型。
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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