  Tracking and identifying athletes on the pitch holds a central role in
collecting essential insights from the game, such as estimating the total
distance covered by players or understanding team tactics. This tracking and
identification process is crucial for reconstructing the game state, defined by
the athletes' positions and identities on a 2D top-view of the pitch, (i.e. a
minimap). However, reconstructing the game state from videos captured by a
single camera is challenging. It requires understanding the position of the
athletes and the viewpoint of the camera to localize and identify players
within the field. In this work, we formalize the task of Game State
Reconstruction and introduce SoccerNet-GSR, a novel Game State Reconstruction
dataset focusing on football videos. SoccerNet-GSR is composed of 200 video
sequences of 30 seconds, annotated with 9.37 million line points for pitch
localization and camera calibration, as well as over 2.36 million athlete
positions on the pitch with their respective role, team, and jersey number.
Furthermore, we introduce GS-HOTA, a novel metric to evaluate game state
reconstruction methods. Finally, we propose and release an end-to-end baseline
for game state reconstruction, bootstrapping the research on this task. Our
experiments show that GSR is a challenging novel task, which opens the field
for future research. Our dataset and codebase are publicly available at
https://github.com/SoccerNet/sn-gamestate.
