## 2 Related Work
The authors used a combination of a convolutional network (CN) and a random decision forest for classifying images of a driver. The authors compared the performance of the Drive-Net with two other popular machine-learning approaches: Recurrent Neural Network (RNN), and a multi-layer perceptron (MLP). Using about 22,425 acquired images under a controlled environment that are publicly available and manually annotated by an expert, Drive-Net achieves a detection accuracy of 95%. [PERSON] et al. [6] proposed a new D-HCNN model based on a decreasing filter size with a smaller number of parameters than that used by models in many other studies. D-HCNN uses HOG feature images, L2 weight regularization, dropout and batch normalization to improve the performance. The authors conducted experimental evaluations on two public datasets: MI-AUC [7] and State Farm [14] with an accuracy of 95.59% and 99.87%, respectively, which represent higher accuracy values than those achieved by many other state-of-the-art methods. There are several other attempts to develop efficient deep learning frameworks to detect distracted drivers such as EfficientDet [15] and other models, described in Ref. [16]. These attempts have analyzed the static characteristics of images. Other attempts, described in Ref. [17] discover the temporal-spatial characteristics of images. Some of these attempts were done by fusing the attention features extracted from the dynamic optical flow information and the spatial feature of the single video frame to recognize the distracted driving posture [17]. Others have combined the handcrafted features and fusion features in a hybrid scheme [18]. [PERSON] et al. [19] developed an algorithm that uses input data generated from a camera inside the car. Such a system is not useful in all vehicles because drivers may easily block it while using a mobile phone and one cannot prohibit the driver from doing it. Another study was done by [PERSON] et al. [20] that intended to get pictures from a front windshield view of the vehicle. First, the driver's face was detected, then Region of Interest (ROI) was identified, and finally mobile phone usage gestures were detected. The detection approach was done by training the system with positive and negative pictures. Positive pictures are with mobile phone usage and negative pictures are without. They proposed two classification architectures, used full and half face images, and the performance in terms of accuracy, specificity, and sensitivity was compared. The evaluation results showed that the performances of two architectures are similar, above 86% for the mobile phone usage detection task. A neural network-based application was developed to detect mobile phone usage [21]. Sample positive (with phone) and negative pictures (without phone) were used for training and testing the cascade object detector on MATLAB. Initial evaluation results showed that the quality of the captured image and the number of images used for training play a major role in the detection accuracy, which was 75%. [PERSON] et al. [22] used Support Vector Machine (SVM) with Polynomial kernel classification to develop an algorithm for extracting characteristics allowing mobile phone identification while driving a vehicle. They used sets of images with 100 positive and other 100 negative images containing frontal images of the driver. The evaluation results showed a success rate of 91.57% for the vision system. For a 3D vision system, [PERSON] and [PERSON] [23] proposed a solution that uses Short-Term (ST) and Long-Term (LT) pattern recognition subsystems to analyze the positions of the driver's hands. The system has three levels of alarms: no alarm, lowest alarm, and highest alarm. ST detects between no alarm or some level alarm. LT is responsible for determining the risk level, either low or high. The classifiers are based on ML and Artificial Neural Networks (ANN).