In this work, we present a pipeline to reconstruct the 3D pose of a horse
from 4 simultaneous surveillance camera recordings. Our environment poses
interesting challenges to tackle, such as limited field view of the cameras and
a relatively closed and small environment. The pipeline consists of training a
2D markerless pose estimation model to work on every viewpoint, then applying
it to the videos and performing triangulation. We present numerical evaluation
of the results (error analysis), as well as show the utility of the achieved
poses in downstream tasks of selected behavioral predictions. Our analysis of
the predictive model for equine behavior showed a bias towards pain-induced
horses, which aligns with our understanding of how behavior varies across
painful and healthy subjects.

本文介绍了一种从 4 个同时监控相机录制的视频中重建马的 3D 姿势的管道，该管道包括训练 2D 无标记姿势估计模型，然后将其应用于视频并执行三角测量。通过数值评估结果（误差分析），以及展示所实现姿势在所选行为预测的下游任务中的实用性，本文证明了模型的准确性。而对于马的预测模型的分析显示出对于疼痛诱发马存在偏见，这与我们对于疼痛和健康主体行为变化的理解是一致的。