  The paper presents a multi-camera tracking method intended for tracking
soccer players in long shot video recordings from multiple calibrated cameras
installed around the playing field. The large distance to the camera makes it
difficult to visually distinguish individual players, which adversely affects
the performance of traditional solutions relying on the appearance of tracked
objects. Our method focuses on individual player dynamics and interactions
between neighborhood players to improve tracking performance. To overcome the
difficulty of reliably merging detections from multiple cameras in the presence
of calibration errors, we propose the novel tracking approach, where the
tracker operates directly on raw detection heat maps from multiple cameras. Our
model is trained on a large synthetic dataset generated using Google Research
Football Environment and fine-tuned using real-world data to reduce costs
involved with ground truth preparation.
