  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.
