Viewpoint-invariant soccer pitch registration using geometric and learned features
Year: 2026
Type: article
Abstract: Automatic registration of broadcast soccer images to a standardized field model enables advanced analytics, augmented reality overlays, and precise player tracking. We propose a fully automatic, viewpoint-independent homography estimation pipeline fusing three complementary geometric cues: white field markings (lines and elliptical arcs), grass-band delimitations, and a binary playing-field mask. Detected primitives are first richly labeled — classifying lines as longitudinal or transversal, characterizing grass-tone transitions, and encoding four-quadrant intersection patterns — to reduce correspondence ambiguity. We then generate and prune candidate subsets of primitives, establish plausible matches to model elements via intersection-pattern rules and projective cross-ratio invariants, and systematically evaluate homography hypotheses using bidirectional mask-projection accuracies and mean reprojection error. An experimental evaluation on the LaSoDa benchmark demonstrates that the proposed method achieves highly accurate registrations with ground-truth primitives and robust performance in the fully automatic end-to-end pipeline. Furthermore, comparative experiments with recent state-of-the-art approaches confirm improved precision and robustness across diverse broadcast scenarios.
Authors: Carlos Cuevas, Daniel Berjón, Narciso García
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,450
OpenAlex ID: W7140125554