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:监控一组目标的相机网络的性能,关键取决于相机的配置。在本文中,我们研究了参数化相机网络模型的重新配置战略,可以同时在全球优化多个目标的感知质量。我们首先提议使用图像中一个单长对象所占用的像素数量,以衡量物体的感测质量,该数量由相机参数(如内在、外向、扭曲系数)决定。然后,我们形成一个单一数量,以测量相机网络对目标的感测质量。这个数量进一步成为我们优化问题的目标功能,以获得最佳的相机配置。我们通过广泛的模拟和实验,核查我们的方法的有效性,并显示其在AprayTag探测任务中的改进性能。这项工作的代码和相关功能是开放的,可在https://github.com/szxc/MultiCam-imulation上查阅。</s>