  Calibration of multi-camera systems, i.e. determining the relative poses
between the cameras, is a prerequisite for many tasks in computer vision and
robotics. Camera calibration is typically achieved using offline methods that
use checkerboard calibration targets. These methods, however, often are
cumbersome and lengthy, considering that a new calibration is required each
time any camera pose changes. In this work, we propose a novel, marker-free
online method for the extrinsic calibration of multiple smart edge sensors,
relying solely on 2D human keypoint detections that are computed locally on the
sensor boards from RGB camera images. Our method assumes the intrinsic camera
parameters to be known and requires priming with a rough initial estimate of
the camera poses. The person keypoint detections from multiple views are
received at a central backend where they are synchronized, filtered, and
assigned to person hypotheses. We use these person hypotheses to repeatedly
solve optimization problems in the form of factor graphs. Given suitable
observations of one or multiple persons traversing the scene, the estimated
camera poses converge towards a coherent extrinsic calibration within a few
minutes. We evaluate our approach in real-world settings and show that the
calibration with our method achieves lower reprojection errors compared to a
reference calibration generated by an offline method using a traditional
calibration target.
