Camera calibration is a necessary preliminary step in computer vision for the estimation of the position of objects in the 3D world. Despite the intrinsic camera parameters can be easily computed offline, extrinsic parameters need to be computed each time a camera changes its position, thus not allowing for fast and dynamic network re-configuration. In this paper we present an unsupervised and automatic framework for the estimation of the extrinsic parameters of a camera network, which leverages on optimised 3D human mesh recovery from a single image, and which does not require the use of additional markers. We show how it is possible to retrieve the real-world position of the cameras in the network together with the floor plane, exploiting regular RGB images and with a weak prior knowledge of the internal parameters. Our framework can also work with a single camera and in real-time, allowing the user to add, re-position, or remove cameras from the network in a dynamic fashion.

相机校准是计算机视觉中估计3D世界中对象位置的必要的初步步骤。尽管可以轻松地离线计算内部摄像机参数，但每次摄像机更改其位置时都需要计算外部参数，因此无法进行快速，动态的网络重新配置。在本文中，我们提出了一种用于评估摄像机网络外部参数的无监督且自动的框架，该框架利用从单个图像中优化的3D人网格恢复功能，并且不需要使用其他标记。我们展示了如何利用常规的RGB图像以及对内部参数的先验知识较弱的情况，检索与网络平面一起的摄像机在网络中的真实位置。