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Keep Your Eye on the Puck: Automatic Hockey Videography

While hockey involves a large playing surface, instantaneous play is typically localized to a smaller region of the ice. Live spectators thus attentively shift their gaze to follow play, and professional sports videographers pan and tilt their cameras to mimic this process. Unfortunately, manual videography is economically prohibitive below the elite level. Here we propose a system for automatically tracking play, allowing a high-definition video feed to be dynamically cropped and retargeted to a spectator's display device. We employ the puck as an objective surrogate for the location of play, and develop a novel method for ground-truthing puck location from high-definition video. This allows us to train a deep network regressor that uses the video imagery, optic flow, estimated player positions and team affiliation to predict the location of play. We show that our algorithm outperforms a simple 'follow the herd' strategy and results in a practical system for delivering high-quality curated video of amateur-level hockey games to remote spectators.