# Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network
## 4 Experiment Results
### Comparison with Existing Systems  
We firstly performed experiments on the AUC Distracted Driver Dataset to evaluate the accuracy of the spatial network. As with the train/test data split proposed in Reference [17], it was found that there was a high correlation between training and test data. In other words, similar images of a person performing one distraction behavior would be in the training and test data. In order to avoid this situation, we re-split the AUC dataset according to the drivers' identifier (ID), and the IDs used in training and test data were separated. The experimental results were compared with Reference [25] as shown in Table 1. \"Original Split\" means the original data split used in Reference [25], and \"Re-Split\" means the data was re-split according to the drivers' IDs. The study applying VGG-16 with regularization was compared. Note that we only evaluated the performance of the spatial stream ConvNet because, in the AUC Distracted Driver Dataset, the sampling rate was low which resulted in discontinuous motion. This would cause very large errors for motion estimation, and the proposed temporal stream ConvNet was not suitable for this case. According to the results, it was found that the data split affected the performance much. The results of using different data splits were quite different. The regularization methodology could increase the accuracy rate by about 1% to 2%.  
Since the displacement of the objects between two consecutive frames is large in the AUC Distracted Driver Dataset, it might cause errors in the process of motion estimation. We compiled our distraction dataset for performance evaluation. In the training process, stochastic gradient descent was used for an optimization learning rate of 0.0001, decay rate of \(10^{-6}\), and momentum value of 0.9. The batch size and number of epochs were set to 16 and 500 respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset** & **Accuracy (\%)** \\ \hline
**Spatial Stream ConvNet** & Original Split & 94.44 \\
**VGG-16 with Regularization**[25] & Original Split & 96.31 \\
**Spatial Stream ConvNet** & Re-Split & 76.25 \\
**VGG-16 with Regularization**[25] & Re-Split & 77.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance evaluation and comparison for spatial stream ConvNet in the AUC Distracted Driver Dataset.  
Figure 6: Self-compiled distraction dataset.  
with the two-stream method [27], and the results are shown in Table 2. Firstly, it was found that the two-stream method [27] and the proposed method indeed increased the accuracy rate of the pre-trained model, especially for the temporal stream ConvNet in the proposed method. Secondly, fusion results were better than using either spatial or temporal stream ConvNet alone. Hence, fusing information is recommended. In addition, the proposed method increased the accuracy rate by nearly 30% compared with Reference [27].  
Table 3 shows the confusion matrix for distraction behavior analysis in the self-compiled dataset. The proposed system provided better results for the actions of _text left_ and _phone left_, while the action of _phone right_ was easily confused with the action of _hair or makeup_.