Sports Camera Pose Refinement Using an Evolution Strategy

Autor:	Grzegorz Rypeść, Grzegorz Kurzejamski, Jacek Komorowski
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Neural and Evolutionary Computing Neural and Evolutionary Computing (cs.NE)
DOI:	10.48550/arxiv.2211.02143
Popis:	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. Comment: Conference paper at 2022 IEEE Congress on Evolutionary Computation (CEC)
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::003d7caa77a2f2b01baf5fa9a33d5a16 Zobrazit plný text záznamu