This paper presents a robust end-to-end method for sports cameras extrinsic parameters optimization using a novel evolution strategy. First, we developed a neural network architecture for an edge or area-based segmentation of a sports field. Secondly, we implemented the evolution strategy, which purpose is to refine extrinsic camera parameters given a single, segmented sports field image. Experimental comparison with state-of-the-art camera pose refinement methods on real-world data demonstrates the superiority of the proposed algorithm. We also perform an ablation study and propose a way to generalize the method to additionally refine the intrinsic camera matrix.

翻译：本文提出了一种鲁棒的端到端方法，利用新型进化策略优化体育摄像机外参。首先，我们构建了面向体育场地边缘或区域分割的神经网络架构；其次，实现了进化策略，其目标是在单幅分割后的体育场图像上精化摄像机外参。与现有顶尖摄像机位姿精化方法在真实数据上的实验对比表明，本算法具有显著优势。我们还进行了消融研究，并提出了将该方法泛化至内参矩阵精化的途径。