Keep Your Eye on the Puck: Automatic Hockey Videography
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".