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.

翻译：相机网络监控一组目标时，其性能关键取决于相机的配置。本文研究参数化相机网络模型的重配置策略，以全局且同步优化多个目标的感知质量。首先，我们提出使用图像中单位长度物体所占像素数作为该物体感知质量的度量，该度量由相机参数（如内参、外参及畸变系数）决定。随后，构建一个单一量值来评估相机网络对目标的感知质量，该量值进一步作为优化问题的目标函数以获取最优相机配置。通过大量仿真与实验验证了我们方法的有效性，结果表明其在AprilTag检测任务上的性能提升。本工作的代码及相关工具已开源，可从https://github.com/sszxc/MultiCam-Simulation获取。