Sports Camera Pose Refinement Using an Evolution Strategy

TL;DR

This paper introduces a new evolution strategy-based method for refining sports camera extrinsic parameters, utilizing a neural network for field segmentation and demonstrating superior performance over existing methods.

Contribution

It presents a novel end-to-end approach combining neural segmentation and evolution strategy for camera pose refinement, with potential for intrinsic parameter optimization.

Findings

Outperforms state-of-the-art camera pose refinement methods

Effective on real-world sports field images

Can be generalized to refine intrinsic camera parameters

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.