## 5 Discussion
### Challenging Cases and Limitations  
In this study, several key challenges were identified in the process of detecting shooting time. First, external factors such as lighting conditions occasionally resulted in insufficient contrast between the bowstring and the background in certain test data. This issue led to reduced accuracy in bowstring detection and, consequently, a relatively high MAE when compared to the actual shooting time. Second, the proposed method for detecting the start of the anchoring phase relies on pose estimation, utilizing three key joint points of the athlete's right arm--shoulder, elbow, and wrist. While this approach is effective when the right arm is fully visible within the video frame, its performance degrades when the arm is partially occluded or moves out of the frame, negatively affecting the accuracy of shooting time detection. Third, in most archery competition videos, the athletes' facial regions are sufficiently large and of high resolution, enabling reliable bowstring detection. However, in some instances, where athletes appear farther from the camera and their faces are smaller, the lower resolution makes bowstring detection more challenging.  
Despite these challenges, the proposed method demonstrated improved accuracy in shooting time detection compared to conventional bowstring-only approaches, even under suboptimal lighting conditions. This improvement is reflected in the results of this study. Additionally, the pose estimation algorithm exhibited robust performance, enabling the detection of the anchoring start point even when parts of the arm were occluded or moved out of the video frame. Consequently, only a minimal number of test cases where the arm was significantly obscured or out of frame were excluded from the analysis.  
To address the issue of low facial resolution caused by increased shooting distance, an alternative shooting time detection method was analyzed, utilizing only the three joint points of the athlete's right arm (shoulder, elbow, and wrist). This method maintained the same criteria for detecting the anchoring start point as the proposed approach. However, for identifying the end point of shooting time, corresponding to the release phase, this method relied on changes in the arm's angle rather than detecting the bowstring. Specifically, it determined the release point by identifying frames where the 10 frame moving average of the right arm's angle exceeded a threshold value D. In this study, D was empirically set to 5\({}^{\circ}\).  
Figure 9 shows the results applied to the existing dataset using only the right arm angle. Compared to Figures 7 and 8, the method using only the right arm angle tended to detect longer shooting times than the proposed method, and tended to detect shorter shooting times than the method using only the bowstring. In addition, the results showed a \(R^{2}\) score of 0.93 and an MAE of approximately 0.512, showing a slight difference compared to the method proposed in this study, but showing improved performance compared to the method using only the bowstring. Additionally, for 11 datasets where bowstring detection was difficult and shooting time could not be measured with the proposed method, the average MAE was confirmed to be 0.54 when only the right arm angle was used. These results show that the proposed method that detects the anchoring start point using the right arm angle and the release point using the bowstring has the best performance, but it can be utilized as a sub-method that can achieve high accuracy in challenging situations where it is difficult to detect the bowstring with only the arm angle. Through this, we confirmed high accuracy even in challenging cases where the bowstring detection is difficult, confirming the possibility of applicability to a wide range of data.