  We address the problem of estimating the 3D pose of a network of cameras for
large-environment wide-baseline scenarios, e.g., cameras for construction
sites, sports stadiums, and public spaces. This task is challenging since
detecting and matching the same 3D keypoint observed from two very different
camera views is difficult, making standard structure-from-motion (SfM)
pipelines inapplicable. In such circumstances, treating people in the scene as
"keypoints" and associating them across different camera views can be an
alternative method for obtaining correspondences. Based on this intuition, we
propose a method that uses ideas from person re-identification (re-ID) for
wide-baseline camera calibration. Our method first employs a re-ID method to
associate human bounding boxes across cameras, then converts bounding box
correspondences to point correspondences, and finally solves for camera pose
using multi-view geometry and bundle adjustment. Since our method does not
require specialized calibration targets except for visible people, it applies
to situations where frequent calibration updates are required. We perform
extensive experiments on datasets captured from scenes of different sizes,
camera settings (indoor and outdoor), and human activities (walking, playing
basketball, construction). Experiment results show that our method achieves
similar performance to standard SfM methods relying on manually labeled point
correspondences.
