TVCalib: Camera Calibration for Sports Field Registration in Soccer

TL;DR

This paper introduces a camera calibration approach for sports field registration in soccer broadcast videos, utilizing a differentiable objective to estimate camera pose and focal length from segment correspondences, enabling accurate 3D registration.

Contribution

It presents a novel differentiable calibration method that estimates camera parameters directly from segment correspondences, and a one-step 3D registration approach outperforming existing methods.

Findings

Achieves superior registration accuracy on two datasets.

Outperforms two state-of-the-art approaches.

Provides a differentiable calibration framework for sports videos.

Abstract

Sports field registration in broadcast videos is typically interpreted as the task of homography estimation, which provides a mapping between a planar field and the corresponding visible area of the image. In contrast to previous approaches, we consider the task as a camera calibration problem. First, we introduce a differentiable objective function that is able to learn the camera pose and focal length from segment correspondences (e.g., lines, point clouds), based on pixel-level annotations for segments of a known calibration object. The calibration module iteratively minimizes the segment reprojection error induced by the estimated camera parameters. Second, we propose a novel approach for 3D sports field registration from broadcast soccer images. Compared to the typical solution, which subsequently refines an initial estimation, our solution does it in one step. The proposed method is evaluated for sports field registration on two datasets and achieves superior results compared to two state-of-the-art approaches.