Predictive Modeling of Equine Activity Budgets Using a 3D Skeleton
  Reconstructed from Surveillance Recordings
Ernest Pokropek Sofia Broomé Pia Haubro Andersen Hedvig Kjellström
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.