Real-time visual play-break detection in sport events using a context descriptor
Why this work is in the frame
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Bibliographic record
Abstract
The detection of play and break segments in team sports is an essential step towards the automation of live game capture and broadcast. This paper presents a two-stage hierarchical method for play-break detection in non-edited video feeds of sport events. Unlike most existing methods, this algorithm performs action and event recognition on content, and thus does not rely on production cues of broadcast feeds. Moreover, the method does not require player tracking, can be used in real-time, and can be easily adapted to different sports. In the first stage, bag-of-words event detectors are trained to recognize key events such as line changes, face-offs and preliminary play-breaks. In the second stage, the output of the detectors along with a novel feature based on spatio-temporal interest points are used to create a context descriptor for the final decision. Experiments demonstrate the efficiency of the proposed method on real hockey game footage, achieving 90% accuracy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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