A Deep Learning Model Based Skill Recognition and Evaluation System for Football Sports Players
Bibliographic record
Abstract
As the world's No. 1 sport with wide popularity and high degree of attention, there exists a great application demand and development potential for applying artificial intelligence to soccer sports training.In this paper, Yolov5s-CBAM target detection network is utilized to identify the human body posture of target athletes in soccer sports training, and HRNet network is used to detect the location information of key points of target human skeleton and identify the skill movements of soccer players.Subsequently, the TDS-Fast DTW algorithm is applied to evaluate the skill movements to establish a skill recognition and evaluation system for soccer sports athletes.It is verified that the soccer player skill movement recognition model proposed in this paper outperforms other comparative models, with the checking rate reaching 99.12%, and the evaluation scores of the model on the skill movements of the athletes are not different from those of the manual evaluation scores (P>0.05).It is also found that the application of the system in actual soccer training matches can fully meet the needs of soccer training.The system in this paper can accurately assess the technical movements of soccer sports athletes to meet the needs of scientific training, and at the same time, it can meet the needs of coaches to timely grasp the understanding of the level of technical movements of soccer athletes and improve the quality of training.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".