Sports Camera Pose Refinement Using an Evolution Strategy
Neural and Evolutionary Computing
2023-10-10 v2
Abstract
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.
Keywords
Cite
@article{arxiv.2211.02143,
title = {Sports Camera Pose Refinement Using an Evolution Strategy},
author = {Grzegorz Rypeść and Grzegorz Kurzejamski and Jacek Komorowski},
journal= {arXiv preprint arXiv:2211.02143},
year = {2023}
}
Comments
Conference paper at 2022 IEEE Congress on Evolutionary Computation (CEC)