  We propose a fully automated system that simultaneously estimates the camera
intrinsics, the ground plane, and physical distances between people from a
single RGB image or video captured by a camera viewing a 3-D scene from a fixed
vantage point. To automate camera calibration and distance estimation, we
leverage priors about human pose and develop a novel direct formulation for
pose-based auto-calibration and distance estimation, which shows
state-of-the-art performance on publicly available datasets. The proposed
approach enables existing camera systems to measure physical distances without
needing a dedicated calibration process or range sensors, and is applicable to
a broad range of use cases such as social distancing and workplace safety.
Furthermore, to enable evaluation and drive research in this area, we
contribute to the publicly available MEVA dataset with additional distance
annotations, resulting in MEVADA -- the first evaluation benchmark in the world
for the pose-based auto-calibration and distance estimation problem.
