Figure 1
- Quantitative analysis of studies carried out in the focus area over the last decade: computer vision being used in soccer
Figure 2
- Methodological approach to APIPS development
Figure 3
- Images manually created to illustrate APIPS flow.
Figure 4
- Detection made by the best model: YOLO v10–L, resolution 1280 and threshold of 50%
Table 1
- F1-score Metrics per Model Configuration
| Configuration | F1-score | Observations |
|---|---|---|
| YOLO v10 | 76.8% | Better than Version 11, but slightly slower inference |
| YOLO v11 | 75.6% | Faster inference |
| Size S | 74.0% | Faster inference, lower F1 |
| Size M | 75.9% | Balanced |
| Size L | 78.5% | Higher inference cost |
| Resolution 640 | 72.3% | Lower performance |
| Resolution 1280 | 80.0% | Best balance between F1 and inference |
| Resolution 1920 | 76.1% | Slower inference |
| Best Configuration | 83.8% | Version 10, Size L, Resolution 1280, 50% threshold; best overall performance |
Table 2
- Results for the best models generated with different parameters of the DeepSORT algorithm
| ID | max_age | n_init | max_cosine | Precision | Recall | F1-score | MOTA | Time (s) |
|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 0.1 | 94.2% | 88.8% | 91.4% | 81.5% | 65 |
| 2 | 1 | 1 | 0.3 | 94.2% | 88.8% | 91.4% | 81.5% | 66 |
| 3 | 3 | 1 | 0.3 | 92.2% | 90.2% | 91.2% | 81.1% | 71 |
| 4 | 3 | 1 | 0.1 | 92.2% | 90.1% | 91.1% | 81.0% | 67 |
| 5 | 3 | 3 | 0.3 | 92.7% | 88.9% | 90.8% | 80.6% | 67 |
| 6 | 3 | 3 | 0.1 | 92.6% | 88.9% | 90.7% | 80.4% | 67 |
| 7 | 1 | 3 | 0.1 | 94.5% | 86.9% | 90.6% | 80.3% | 65 |
Figure 5
- Example of partial occlusion: player begins to leave the camera’s field of view in the frame presented in (d). In (e) there is an exchange of the identifier
Table 3
- Results of the proposed methodology for team classification
| Class | Precision | Recall | F1-score |
|---|---|---|---|
| Team A | 86.2% | 97.5% | 91.5% |
| Team B | 93.5% | 95.7% | 94.6% |
| Referee Team | 89.6% | 53.7% | 67.1% |
Table 4
- Results of the models with the best parameters, for different resolutions
| Resolution | Total Accuracy | Conditional Accuracy (visible digit) | Time (s) |
|---|---|---|---|
| 640 | 1.73% | 5.07% | 883 |
| 512 | 5.34% | 15.73% | 740 |
| 256 | 3.30% | 20.49% | 232 |
| 128 | 1.86% | 26.22% | 101 |
| 64 | 0.16% | 8.77% | 69 |
Figure 6
- Example of possible cases during the process of identifying the player: (a) only part of the number is visible – wrong identification; (b) both digits are visible – correct identification; (c) no digit is visible – no identification.
Figure 7
- TTI with Combined Digit Highlighting – spreads the identification even when the number is not visible.
Table 5
- Results obtained with (and without for reference) TTI considering the different cases of heuristics.
| Heuristic | Total Accuracy |
|---|---|
| No TTI | 5.34% |
| TTI – Highest Frequency | 12.71% |
| TTI – Highest Confidence | 18.19% |
| TTI – Highest Average Confidence | 13.74% |
| TTI – Combined Digit Highlighting | 26.27% |
Table 6
- Performance of the positioning methodology at different distance error tolerances.
| Position Error Tolerance (meters) | %Hits |
|---|---|
| 1 | 66.5% |
| 3 | 89.6% |
| 5 | 91.0% |
| 10 | 91.6% |
| 20 | 92.9% |
| 30 | 95.0% |
Figure 8
- Example output from the positioning step – the obtained positions are resized to the 2D field representation inserted in the original image. The colors of the teams are followed in 2D field. Referees are yellow.