# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 4 Discussion  
In our research, we developed and implemented a bespoke ID tag identification algorithm called BAIT, which exhibited a notably higher level of effectiveness in comparison to conventional, standard software solutions (compared to Pupil Labs software). This enhanced performance was particularly evident in the algorithm's ability to manage and compensate for certain challenges inherent in the ID tag identification process. The following methods were used for higher identification efficiency:  
1. Binarization process on the three distinct RGB components of a frame, executed at various sensitivity levels. 2. Interpolation between successive video frames. 3. Adding predefined offset. A key limitation encountered in ID tag tracking is the obstruction of the ID tag's visibility. Naturally, when an ID tag is completely obscured from the camera's view, its identification becomes unfeasible using direct visual methods. However, our algorithm demonstrates a significant strength in dealing with transient occlusions or minor alterations in the viewing angle during such occlusions. This is achieved through a robust interpolation technique, which is a critical component of our algorithm. The interpolation method \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Windscreen**} & \multicolumn{2}{c}{**Dashboard**} \\ \cline{2-5}  & **Pupil Labs Software** & \multirow{2}{*}{**BAIT (\%)**} & \multirow{2}{*}{**Pupil Labs Software** (\%)**} & \multirow{2}{*}{**BAIT (\%)**} \\  & **(\%)** & & & \\ \hline Mean & 17.88 & 95.35 & 19.53 & 67.74 \\ Mean Deviation & 8.40 & 5.04 & 9.89 & 19.08 \\ Standard Deviation & 9.99 & 8.82 & 11.65 & 24.43 \\ CI 95\% & 3.10 & 2.73 & 3.61 & 7.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Filtered statistical values of comparison where 0 values were excluded. Figure 8: The results are visualized as percentages, with the position of the ID tags reflecting their actual placement. employed is designed to predict the position of an ID tag during short periods when it is not visible, using the data from frames where the tag is clearly identified. Our study evaluates the performance of the 'BAIT' against the 'Pupil Labs software' in attention detection, revealing key differences in accuracy and consistency across different focus areas like the windscreen and dashboard, and highlighting potential areas for further research in algorithm optimization for specific tasks as follows:  
* Algorithm Performance: The 'BAIT' demonstrated a markedly higher mean attention percentage on the windscreen, suggesting enhanced detection capabilities in this area compared to the 'Pupil Labs software'. While the 'BAIT' also exhibited a higher mean attention percentage for the dashboard, the increment was not as substantial as observed in the windscreen condition. * Consistency of Results: The reduced mean deviation in the 'BAIT' for the windscreen condition implies a more consistent measurement of focus across trials than the 'Pupil Labs software'. However, the larger mean deviation in the dashboard condition for the 'BAIT' indicates greater variability, which could be attributed to specific challenges in this setting or inherent algorithmic differences. * Variability of Data: Standard deviation assessments reveal that the 'BAIT' yields more consistent results for the windscreen condition. Conversely, the 'Pupil Labs software' appears to provide more consistent outcomes when measuring focus on the dashboard. * Confidence in Estimates: The narrower confidence interval for the 'BAIT' in windscreen observations suggests a higher level of precision in these estimates, potentially reflecting a more reliable performance in this specific task. * Implications for Future Research: Future research could benefit from further investigation into the conditions and parameters under which each algorithm operates most effectively, as suggested by the observed variability in standard deviation and confidence intervals.