Camera Selection for Broadcasting Soccer Games
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When broadcasting events such as soccer games, human operators constantly select the camera with the best viewpoint to cover the whole event. Modeling the prediction of which camera should be on air will assist automatic sports broadcasts and influence millions of viewers. In this paper, we propose a proof-of-concept method to automatically select cameras for broadcasting soccer games. First, a random forest based regressor smoothly predicts the visual importance of short video clips using deep convolutional features. Then, the predictions from multiple candidate cameras are regularized by a novel camera duration cumulative distribution function (CDF), naturally guiding the camera selection. We apply our approach to real soccer broadcasts with a professional human operator's result as a reference. The quantitative experiments demonstrate that our method outperforms two alternatives in terms of prediction accuracy. Moreover, the video generated by our method is preferred in the user study experiment, exhibiting its practicality.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it