Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany.
Department of Biology, University of Konstanz, Konstanz, Germany.
Elife. 2019 Oct 1;8:e47994. doi: 10.7554/eLife.47994.
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, , that addresses these problems using an efficient multi-scale deep-learning model, called , and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.
定量行为测量对于回答跨科学领域的问题非常重要，从神经科学到生态学。最先进的深度学习方法通过允许研究人员直接从图像或视频中自动估计动物身体部位的位置，在数据质量和细节方面取得了重大进展。然而，目前可用的动物姿势估计方法在速度和鲁棒性方面存在局限性。在这里，我们介绍了一个新的易于使用的软件工具包 ， ，它使用高效的多尺度深度学习模型 ， ，和一个快速的基于 GPU 的峰值检测算法来解决这些问题，用于以亚像素精度估计关键点位置。与目前可用的方法相比，这些改进提高了处理速度 >2x，而不会损失准确性。我们通过在实验室和野外环境中进行多个具有挑战性的动物姿势估计任务来展示我们方法的多功能性，包括相互作用的个体群体。我们的工作降低了使用先进的行为测量工具的障碍，并且在行为科学中有广泛的适用性。
