VICE: View-Invariant Chess Estimation

Kevin Zhu, Alexander Wong, John McPhee

2023

Abstract

A digitized chess match offers chess players a convenient way to study previous matches. However, manually recording a large number of matches can be laborious, while automated methods are usually hardware-based, requiring expensive chessboards. Computer vision provides a more accessible way to track matches from videos. However, current vision-based digitizers are often evaluated on images captured by cameras placed directly above a chessboard, and performance suffers when the camera angle is lower, limiting their applicability. Motivated to develop a more practical solution, we introduce VICE, a view-invariant chess estimator to digitize matches from camera angles not seen during training. Due to its small model size and computational efficiency, VICE is suitable for mobile deployment. By rearranging the framework for chess detection and incorporating prior information from chess and basic geometry, we simplify the chess estimation problem and mitigate the challenges that current chess digitizers struggle with, such as occlusion. We combine the board localization and chess piece detection phases of classical two-step chess estimation to develop a prototype for the first single-step chess digitizer. We show that, with minimal training data, our prototype can infer moves from camera angles that current chess digitizers cannot, while being much smaller in size.

Paper Citation

in Harvard Style

Zhu K., Wong A. and McPhee J. (2023). VICE: View-Invariant Chess Estimation. In Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS; ISBN 978-989-758-673-6, SciTePress, pages 50-60. DOI: 10.5220/0012167200003587

in Bibtex Style

@conference{icsports23,
author={Kevin Zhu and Alexander Wong and John McPhee},
title={VICE: View-Invariant Chess Estimation},
booktitle={Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS},
year={2023},
pages={50-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012167200003587},
isbn={978-989-758-673-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS
TI - VICE: View-Invariant Chess Estimation
SN - 978-989-758-673-6
AU - Zhu K.
AU - Wong A.
AU - McPhee J.
PY - 2023
SP - 50
EP - 60
DO - 10.5220/0012167200003587
PB - SciTePress