Players Tracking and Identification for Minimap Generation in American Football

This study focuses on reconstructing game states in American football by generating minimap representations of plays from video footage. Our approach follows a five-step pipeline: detecting and tracking players and referees, identifying players via jersey numbers, transforming camera perspectives, stitching multiview images to create a panoramic image, and generating a minimap. Obtaining accurate player trajectories requires a sideline perspective wide view, providing a comprehensive field overview. Conversely, jersey number recognition demands an end-zone perspective tight view, where players face each other before playing. To integrate these perspectives, we apply image stitching to merge multiple camera views into a seamless scene. Camera transformation techniques ensure accurate spatial alignment across perspectives. Our pipeline achieved 59.9 mean localization error with 45.6% accuracy for team classification for players and referees. This work introduces an efficient pipeline for automated minimap generation, enabling effective game state reconstruction from video footage.