  The performance of a camera network monitoring a set of targets depends
crucially on the configuration of the cameras. In this paper, we investigate
the reconfiguration strategy for the parameterized camera network model, with
which the sensing qualities of the multiple targets can be optimized globally
and simultaneously. We first propose to use the number of pixels occupied by a
unit-length object in image as a metric of the sensing quality of the object,
which is determined by the parameters of the camera, such as intrinsic,
extrinsic, and distortional coefficients. Then, we form a single quantity that
measures the sensing quality of the targets by the camera network. This
quantity further serves as the objective function of our optimization problem
to obtain the optimal camera configuration. We verify the effectiveness of our
approach through extensive simulations and experiments, and the results reveal
its improved performance on the AprilTag detection tasks. Codes and related
utilities for this work are open-sourced and available at
https://github.com/sszxc/MultiCam-Simulation.
