VICE: View-Invariant Chess Estimation

Kevin Zhu, Alexander Wong, John McPhee

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 c