Advancing Player Identification and Tracking with Global ID Fusion (GIF)

Rapid player motion, occlusions, substitutions, and jersey changes pose major challenges for identity-consistent tracking in sports. Existing multi-object tracking (MOT) methods struggle in long-term and multi-perspective settings like broadcast footage, where views change frequently. To address this, we first introduce MuPNIT, the first MOT and ReID benchmark to capture these multi-perspective dynamics along with long-term appearance variations across seasons, teams, and jersey changes. Second, we propose Global ID Fusion (GIF), a novel context-aware tracking-with-identification framework that enables robust tracking of both seen and unseen players. Unlike prior approaches, GIF performs single pass global ID association and supports zero-shot identity recognition. Our approach achieves state-of-the-art results, improving HOTA by 25.3% and IDF1 by 79.5% over OC-SORT. Finally, to assess identity consistency, we introduce five Global ID metrics that reveal tradeoffs in tracking stability. By bridging MOT and ReID, our work advances identity-aware player tracking in sports and sets a new benchmark with applications in sports analytics, surveillance, and long-term person search.