Visual Image Recognition of Basketball Turning and Dribbling Based on Feature Extraction
Bibliographic record
Abstract
The processing of basketball videos with complex contents faces several challenges in terms of global motion features, group motion features, and individual pose features. The current research cannot solve problems, such as the diverse spatiotemporal features of actions, the utilization of correspondence between spatiotemporal features, the increase of data volume, and the complexity of the network. To solve these problems, this paper studies the visual image recognition of basketball turning and dribbling based on feature extraction. Specifically, the optical flow image was introduced to establish the relationship between the velocity field of the basketball turning and dribbling and the grayscale of the image frame, such as to effectively depict the time variation of pixels. In addition, a convolutional neural network was established based on multi-feature learning to process the sports video image frames, and to extract more spatiotemporal features of basketball turning and dribbling. To improve the feature utilization of the action recognition model, this paper strengthens the extraction of dynamic and static features for the recognition of the player's basketball turning and dribbling in the same scene, and improves the existing convolutional neural network. Furthermore, the multi-feature learning of motion excitation and temporal aggregation of actions were completed. The proposed model was proved effective through experiments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".