Validating marker-less pose estimation with 3D x-ray radiography

These data were generated to evaluate the accuracy of DeepLabCut (DLC), a
deep learning marker-less motion capture approach, by comparing it to a 3D
x-ray video radiography system that tracks markers placed under the skin
(XROMM). We recorded behavioral data simultaneously with XROMM and RGB
video as marmosets foraged and reconstructed three-dimensional kinematics
in a common coordinate system. We used XMALab to track 11 XROMM markers,
and we used the toolkit Anipose to filter and triangulate DLC trajectories
of 11 corresponding markers on the forelimb and torso. We performed a
parameter sweep of relevant Anipose and post-processing parameters to
characterize their effect on tracking quality. We compared the median
error of DLC+Anipose to human labeling performance and placed this error
in the context of the animal's range of motion.

本数据集的生成旨在评估DeepLabCut（DLC）的追踪精度——该方法是一种无标记点深度学习运动捕捉技术——并将其与可追踪皮下植入标记点的三维X射线透视影像系统（XROMM）进行对比。研究过程中，我们在普通狨猴觅食时，同步采用XROMM与RGB视频采集行为学数据，并在统一坐标系下重构其三维运动学特征。我们通过XMALab追踪了11个XROMM标记点，并借助工具包Anipose对前肢与躯干上对应11个标记点的DLC追踪轨迹开展滤波与三角化处理。我们对Anipose相关参数及后处理参数开展参数扫描实验，以明确这些参数对追踪质量的影响。我们将DLC+Anipose系统的中位误差与人工标注性能进行对比，并结合受试动物的关节活动范围对该误差值进行情境化分析。