Automatic audience-informed video summarization of hockey broadcasts
Bibliographic record
Abstract
In the past decade, online video has risen to become one of the most common ways of sharing and consuming media.With so much video content readily-available, browsing or processing these data is difficult due to the long duration of a typical video.Video summaries, on the other hand, provide an effective way for viewers to enjoy the highlights of a video without having to watch the entire video.In this work, we propose a system that automatically summarizes hockey broadcasts into highlight videos.We leverage the audience of a game-both the broadcasters and the crowd-to decide which shots should be included in the final highlight reel.We argue that since the game is directly sourced by the crowd and the broadcasters, our direct approach is more effective than similar techniques in the literature which rely on external annotators.Our system accomplishes this by first extracting video and audio features using convolutional neural networks (CNN) and searching for key indicators of excitement (e.g.cheering or celebration).Lastly, the best highlights are dynamically selected under a set of constraints (such as output duration) and composited into the final summary.Our results demonstrate that this technique accurately extracts the most exciting moments of a hockey game while avoiding the bias from using predetermined metrics or external annotations.i
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".