Approaching In-Venue Quality Tracking from Broadcast Video using Generative AI

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Harry Hughes, Michael Horton, Felix Wei, Michael Stokl, Harshala Gammulle, Clinton Fookes, Sridha Sridharan, Sateesh Pedagadi, Patrick Lucey


Over the last 25 years, soccer tracking data has provided a deeper understanding of the ways that players and teams play the game. Although traditional tracking systems require in-venue installation, there is a current push to track players remotely from broadcast footage. However, tracking data obtained from broadcast footage is inherently incomplete due to players being out of the broadcast camera’s field of vision. We address this issue in this paper, leveraging generative AI to predict highly accurate locations of the players for the large portions of games where they cannot be visually perceived.