This work introduces a novel end-to-end approach for estimating extrinsic parameters of cameras in multi-camera setups on real-life sports fields. We identify the source of significant calibration errors in multi-camera environments and address the limitations of existing calibration methods, particularly the disparity between theoretical models and actual sports field characteristics. We propose the Evolutionary Stitched Camera calibration (ESC) algorithm to bridge this gap. It consists of image segmentation followed by evolutionary optimization of a novel loss function, providing a unified and accurate multi-camera calibration solution with high visual fidelity. The outcome allows the creation of virtual stitched views from multiple video sources, being as important for practical applications as numerical accuracy. We demonstrate the superior performance of our approach compared to state-of-the-art methods across diverse real-life football fields with varying physical characteristics.

该研究提出了一种新颖的端到端方法，用于估计多摄像机设置中实际运动场上的摄像机外参参数。通过识别多摄像机环境中校准误差的来源并解决现有校准方法的局限性，尤其是理论模型与实际运动场特点之间的差异，我们提出了演化缝合摄像机校准（ESC）算法。该算法包括图像分割和演化优化新型损失函数，为多摄像机校准提供了统一准确的解决方案，并具有高视觉保真度。这一成果能够从多个视频源创建虚拟拼接视图，对于实际应用和数值准确性同等重要。我们在具有不同物理特征的各种真实足球场地上展示了我们方法相对于最先进方法的卓越性能。

野外环境下的进化缝合相机标定