## 8 Discussion  
In this work, we compared our proposed approach with six networks (ResNet-50, ResNet-101, VGG-19, MobileNet, InceptionV3, and Xception) for a two-category classification problem of distraction detection (namely, texting--right, talking on the phone--right, texting--left, talking on the phone--left, operating the radio, drinking, reaching behind, hair and makeup, and talking to passenger distractions). The proposed approach, based on the features obtained from the last pooling layer of ResNet-50 followed by the classification layer consisting of the SVM, outperformed the existing approaches and the state-of-the-art networks on SFDDD, DrivFace, BU, and FT-UMT detection datasets, as can be seen from the results presented in Section 6. Figure 7 shows that our proposed approach outperformed ResNet-50, ResNet-101, VGG-19, MobileNet, InceptionV3, and Xception, in terms of accuracy (with a maximum accuracy of 93.44%), whereas other methods exhibited lower accuracy with VGG-19 performing worst of all. The reason for the good performance of our proposed approach is the optimal classification capability of SVM on ResNet-50 features in Figures 12 and 13 showing the scatter plot for the first two principal components of features extracted from ResNet-50. This figure gives the visualization of features of the SFDDD dataset that are obtained by applying principal component analysis. The low performance of VGG-19 is probably due to the vanishing gradient problem which is well addressed in the architecture of ResNet. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **[PERSON] [45]** & **[PERSON] [46]** & **[PERSON] [44]** & **[PERSON] [43]** & **ReSVM** \\ \hline BU & 85.00 & 89.20 & 79.63 & 81.40 & **90.46** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison using BU dataset. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **[PERSON] [46]** & **[PERSON] [51]** & **[PERSON] [52]** & **[PERSON] [53]** & **ReSVM** \\ \hline DrivFace & 92.35 & 93.00 & 93.20 & 81.00 & **93.44** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison using DrivFace dataset. Table 10 shows the interclass and intraclass distances for the features extracted from the last pooling layer of ResNet-50 for the SFDDD dataset. We can see that the interclass variation is higher than the intraclass variation, e.g., interclass distance of class 3 and class 4 is significantly higher than the intraclass distances shown in the diagonal. From the table, it can be seen that the interclass distance between classes 4 and 1 is maximum, i.e., 4. Equations (9) and (10) show the formulae for computing average distance and average linkage for intra- and interclass distances, respectively. In Table 10, the value 0.5 shows that the distance between those two classes is very small, i.e., high similarity exists. As there are many classes having high similarity, therefore, the value of 0.5 occurs frequently in the table. Figure 12: Scatter plot showing the features (first two principal components) of pooling layer of proposed approach of SFDDD dataset for driver distraction detection including safe driving, texting—right, talking on the phone—right, texting—left, talking on the phone—left, operating the radio, drinking, reaching behind, hair and makeup, and talking to a passenger. Figure 13: Linear projection showing the features (first two principal components) of pooling layer of proposed approach of SFDDD dataset for driver distraction detection including safe driving, texting—right, talking on the phone—right, texting—left, talking on the phone—left, operating the radio, drinking, reaching behind, hair and makeup, and talking to a passenger. \[S_{a}=\frac{\sum_{i,i^{\prime}}\parallel x_{i}-x_{i^{\prime}}\parallel}{N_{k}(N_{k} -1)} \tag{9}\]  
Here, \(x_{i}\) is the number of intraclass feature attributes, \(x_{i}-x_{i^{\prime}}\) is distance, and \(N_{k}\) is total number of vectors.