  We consider the task of re-calibrating the 3D pose of a static surveillance
camera, whose pose may change due to external forces, such as birds, wind,
falling objects or earthquakes. Conventionally, camera pose estimation can be
solved with a PnP (Perspective-n-Point) method using 2D-to-3D feature
correspondences, when 3D points are known. However, 3D point annotations are
not always available or practical to obtain in real-world applications. We
propose an alternative strategy for extracting 3D information to solve for
camera pose by using pedestrian trajectories. We observe that 2D pedestrian
trajectories indirectly contain useful 3D information that can be used for
inferring camera pose. To leverage this information, we propose a data-driven
approach by training a neural network (NN) regressor to model a direct mapping
from 2D pedestrian trajectories projected on the image plane to 3D camera pose.
We demonstrate that our regressor trained only on synthetic data can be
directly applied to real data, thus eliminating the need to label any real
data. We evaluate our method across six different scenes from the Town Centre
Street and DUKEMTMC datasets. Our method achieves an improvement of $\sim50\%$
on both position and orientation prediction accuracy when compared to other
SOTA methods.
