Puck localization and multi-task event recognition in broadcast hockey\n videos
Bibliographic record
Abstract
Puck localization is an important problem in ice hockey video analytics\nuseful for analyzing the game, determining play location, and assessing puck\npossession. The problem is challenging due to the small size of the puck,\nexcessive motion blur due to high puck velocity and occlusions due to players\nand boards. In this paper, we introduce and implement a network for puck\nlocalization in broadcast hockey video. The network leverages expert NHL\nplay-by-play annotations and uses temporal context to locate the puck. Player\nlocations are incorporated into the network through an attention mechanism by\nencoding player positions with a Gaussian-based spatial heatmap drawn at player\npositions. Since event occurrence on the rink and puck location are related, we\nalso perform event recognition by augmenting the puck localization network with\nan event recognition head and training the network through multi-task learning.\nExperimental results demonstrate that the network is able to localize the puck\nwith an AUC of $73.1 \\%$ on the test set. The puck location can be inferred in\n720p broadcast videos at $5$ frames per second. It is also demonstrated that\nmulti-task learning with puck location improves event recognition accuracy.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".