# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 3 Results  
Table 2 presents the success rates of identifying windscreen boundary tags in 10 different measurements. These rates are compared between the standard software provided with Pupil Core ET glasses and the proprietary algorithm developed for the study, highlighting the effectiveness of each method in tag detection under varying conditions. To compare the performance of 'Pupil Labs software' and 'BAIT', we conducted a comprehensive statistical analysis. We first assessed the normality of our data distributions using the Shapiro-Wilk test. The results showed a significant deviation from normality for both 'Pupil Labs software' (\(p\) = 0.0005) and 'BAIT' (\(p\) < 0.0001), indicating that neither dataset followed a normal distribution. This finding highlights the need for caution when using tests that assume normality. Additionally, the [PERSON]'s test for the equality of variances indicated a significant difference in variances between the groups (\(p\) = 0.00065), suggesting a violation of the homogeneity of variances assumption. Based on these preliminary findings, we chose statistical methods that do not depend on the assumption of equal variances between groups for accurate analysis. Due to the deviations from normality and homogeneity of variances, we opted to use the Wilcoxon Signed-Rank Test for our analysis. The results of the Wilcoxon Signed-Rank Test indicate a highly significant difference in median values between 'Pupil Labs software' and 'BAIT' (\(p\) < 0.00001), confirming a statistically significant difference in performance between the two conditions. The 'Pupil Labs Software' failed to recognize ID tag 7 in 5 out of 10 instances, while 'BAIT' performed the worst at number 9 due to the participant head movements (looking down) and the camera failing to capture the ID tag. The 'BAIT' consistently outperformed the 'Pupil Labs software' across all measured conditions in accuracy and reliability. It effectively addresses challenges such as brief occlusions or slight angle changes in ID tags, which are common in tracking scenarios. Through sophisticated interpolation techniques, our algorithm compensates for moments when an ID tag is temporarily obscured or viewed from different angles, thereby minimizing errors in tracking and ensuring continuous and precise ID tag identification. This capability is crucial for applications requiring uninterrupted monitoring and exact location tracking. Figure 7: Incorrect recognition of the dashboard area-gaze position (yellow circle with green fill), detected tags (blue squares), compensated tag positions (dots in the corners) and “gaze on dashboard” indication (red dots). Analysis of the performance data across ID tags 4 to 7 revealed that the mean percentage of success for the 'BAIT' ranged from 92.74% to 98.05%, with relatively low mean deviations and standard deviations, indicating consistent performance across the trials, as shown in Table 3. In contrast, the 'Pupil Labs software' showed lower mean performances with greater variability. \begin{table}
\begin{tabular}{c c c c} \hline
**No. ** & **ID Tag No. ** & **Pupil Labs Software (\%)** & **BAIT (\%)** \\ \hline
1 & tag 4 & 20 & 99.50 \\
1 & tag 5 & 2  
Table 4 compares the effectiveness of 'Pupil Labs software' with a custom-developed algorithm in detecting dashboard boundary tags across 10 distinct measurements. This comparison serves to demonstrate the relative efficiency of each method in ID tag identification under various conditions. To evaluate the comparative performance of 'Pupil Labs software' and 'BAIT', we conducted a comprehensive statistical analysis. We ensured the robustness of our findings by first running the Shapiro-Wilk test to assess the normality of our data distributions. The results show that 'Pupil Labs software' did not significantly deviate from a normal distribution (\(p\) = 0.097), however, 'BAIT' exhibited a significant deviation from normality (\(p\) = 0.011), suggesting that its data did not follow a normal distribution.