  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.
