Indexing of NFL Video using MPEG-7 Descriptors and MFCC features
Bibliographic record
Abstract
In this paper, we propose an application system to classify American football (NFL) video shots into 4 categories, namely: pass plays, run plays, field goal/extra point plays (FG/XP) and kickoff/punt plays (K/P). The proposed system consists of two stages. The first stage is responsible for play event localization and the latter stage is responsible for feature mapping and classification. For play event localization we have proposed an algorithm that uses MPEG-7 motion activity descriptor and mean of the magnitudes of motion vectors, in a collaborative manner to detect the starting point of a play event within a video shot with 83% accuracy. The indexing and classification stage uses MPEG-7 motion and audio descriptors along with Mel Frequency Cepstrum Coefficients (MFCC) features to classify the events into 4 categories using Fisher's LDA. We obtain indexing accuracy of 92.5% by using a leave-one-out classification technique on a database of 200 video shots taken from 4 different games obtained from 4 different networks.
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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 | 0.000 |
| 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 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".