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
Computer Science and Engineering, University of Minnesota, Minneapolis, USA.
California National Primate Research Center, Davis, USA.
Int J Comput Vis. 2023 Jan;131(1):243-258. doi: 10.1007/s11263-022-01698-2. Epub 2022 Oct 16.
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.
在非人类灵长类动物在世界中移动时自动估计其姿势的能力，对生物学和生物医学的几个子领域来说都很重要。受基准挑战（如目标检测）推动的计算机视觉模型近期成功的启发，我们提出了一个名为“开放猴子挑战”的新基准挑战，通过年度竞赛促进集体社区努力，以构建可推广的非人类灵长类动物姿势估计模型。为举办该基准挑战，我们提供了一个新的公共数据集，该数据集由111,529张带注释（17个身体地标）的非人类灵长类动物照片组成，这些照片取自包括互联网、三个国家灵长类动物研究中心和明尼苏达动物园等各种来源的自然场景。此类带注释的数据集将用作训练和测试数据集，以开发具有标准化评估指标的可推广模型。我们通过基于七个最先进的姿势估计模型将我们的数据集与现有数据集进行比较，定量地证明了我们数据集的有效性。
