Action Detection in Badminton Courts Using AVA Dataset and MMAction2 Architecture with Slow Fast Model
Bibliographic record
Abstract
As a matter of fact, action detection technologies have emerged as powerful tools for a multitude of applications, from surveillance and security to healthcare and sports analytics. Badminton courts, especially those that are highly frequented, present unique challenges owing to the concentration of people engaged in diverse activities. Incidents such as unintended collisions between players, or unauthorized walking across the courts, are not uncommon and necessitate efficient monitoring for risk mitigation. This study addresses these issues by employing the MMAction2 architecture and the Slow Fast model for action detection in badminton courts. The author uses a dataset collected from multiple badminton facilities and leverages the AVA dataset for training and validation. The results are promising, with the model showing high levels of accuracy in identifying various types of actions: playing badminton, sitting, walking across the court, falling, and watching the game. The implications of this research are significant for badminton court management and safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".