  Designing robust activity detectors for fixed camera surveillance video
requires knowledge of the 3-D scene. This paper presents an automatic camera
calibration process that provides a mechanism to reason about the spatial
proximity between objects at different times. It combines a CNN-based camera
pose estimator with a vertical scale provided by pedestrian observations to
establish the 4-D scene geometry. Unlike some previous methods, the people do
not need to be tracked nor do the head and feet need to be explicitly detected.
It is robust to individual height variations and camera parameter estimation
errors.

Computer Vision and Pattern Recognition;Computation and Language