## 4 Experimental Result
### Experimental Result  
This study calculated the IoU to analyze the accuracy of the generated bird's-eye view court image. Image labeling was performed with the Labellmg tool, consisting of the Left Court Paint Zone (Left box), Right Court Paint Zone (Right box), full court (Full Court), Left Court 3 Point Line (Left Semicircle), Right Court 3 Point Line (Right Semicircle), Left Half Court (Left Court), Right Half Court (Right Court), and Court Center Circle (Mid Circle). With the ROI (Region of Interest) coordinate, a polygon-shaped BitMask is generated, and the intersection and union of the BitMask is calculated, allowing derivation of the IoU by Function 1. The calibrated result of the court is shown on Figure 7. The colored lines are the calibrated result of the model. \[\text{IoU}=\frac{\text{Overlapping Area}}{\text{Union Area}} \tag{1}\]  
Figure 6: Application screenshot. Figure 7: Calibrated result of court. At the calibration stage, the target value and the outcome value have been compared. As a result, the IoU value for recognizing the whole court showed about 97%, the left court 3 point line showed 96%, the left court paint zone showed about 94%, the left court half line showed about 95%, the right court half line showed about 95%, the court center circle showed about 95%, the right court 3 point line showed 94%, and the right court paint zone showed about 94% accordant accuracies each. The average court accordant IoU showed about 95.4%. The whole result is shown on Table 1. Partial IoU Ranges represent the area of courts and IoUpart represents its accuracy. This result outperformed the previous study of [PERSON] et al. which showed an accuracy of 80.5% [8]. It also outperformed the previous study of [PERSON] et al., which had been studied with End-to-End process and showed an accuracy of 83.2% [9]. Overall, our model improved the result compared to the previous studies from about 12.2% to 14.9%. For the action recognition model, 10% of the dataset was randomly allocated as test data. The models trained with YOLO used split data from each Roboflow dataset. Specifically, for athlete recognition, 87%/6%/6% of the data were allocated for training, validation, and testing, respectively. For court segmentation, 87%/9%/4% of the data were used, and for uniform classification, 80%/13%/7% of the data were applied. The results were analyzed based on these allocations. The segmentation result is shown in Figure 8. The center circle, paint zone, basketball court, and 3-point line showed an average of 92.5% accuracy and, in particular, the segmentation result for the paint zone and 3-point line showed a high 98% accuracy, which indicates that the model showed a significant impact. For the background, our study applied an additional algorithm to recognize the non-court part as a background, so no additional accuracy calculation has been taken. Overall, excluding the background, the model performed with an average of 92.46% accuracy of court segmentation. The detection and classification for player uniforms, teams, and actions are shown in Figures 9 and 10. Figure 9 shows the confusion matrix result for player actions and results in about an 85.04% average accuracy for detecting the actions. In particular, the model shows a high classification result for specific important actions in games, such as blocking or shooting. The cases with the lowest accuracy in the analysis results are 'Ball in Hand', 'Defense', and 'No Action'. For 'Ball in Hand,' the low accuracy might be attributed to the fact that this action often overlaps with dribbling or passing, as players tend to hold the ball momentarily during these actions. Therefore, it is anticipated that accuracy