# A Data Augmentation Approach to Distracted Driving Detection
## 1 Introduction
A combination of pre-trained sparse filters and convolutional neural networks [16] was used to increase the classification accuracy of the SUE-DP dataset to 99.78%. [PERSON] [17] improved the conventional regions with CNNs features (R-CNN) framework by replacing traditional skin-like region extractor algorithms and obtained a classification accuracy of 97.76% on the SUE-DP dataset. [PERSON] [18] used an improved dual-input deep three-dimensional convolutional network structure algorithm based on a three-dimensional convolutional neural network (3 DCNN), achieving 98.41% accuracy on the rail transit dataset. [PERSON] [19] trained two independent convolutional neural networks by optimizing the size and number of convolution kernels, which can effectively identify mobile phones and hands in real time achieving 144 fps and an accuracy of 95.7% for mobile phone usage on the self-built dataset. Multiscale attention convolutional neural network [20] was proposed driver action recognition. More recent datasets and studies include: The StateFarm dataset was published in the 2016 Kaggle distracted driving recognition competition [21] with ten types of distracted driving behaviors. [PERSON] [22] used an improved multiscale Inception model with a classification accuracy of 96.23% on the StateFarm dataset. [PERSON] et al. [23] used the improved deformable and dilated faster RCNN (DD-RCNN) structure to obtain an accuracy of 92.2%. [PERSON] [24] and [PERSON] [25] used the 3 DCNN algorithm to recognize driving behavior and got 96.67% and 94.4% accuracy rates, respectively. In 2018, [PERSON] and others [26; 27] proposed the American University in Cairo (AUC) distracted driving dataset with reference to the ten distracted postures defined in the StateFarm dataset. The ASUS ZenPhone close-range camera and DS325 Sony DepthSense camera were used to collect driving images and videos of 44 volunteers from 7 countries. The cameras were fixed to the roof handle on the top of the front passenger's seat. The resolution of the data is 1080 \(\times\) 1920 or 640 \(\times\) 480. A total of 17,308 frames were collected for the dataset, which was finally annotated to ten kinds of distracted driving behaviors: safe driving (3686), texting using the right hand (1974), talking on the phone using the right hand (1223), texting using left hand (1301), talking on the phone using left hand (1361), operating the radio (1220), drinking (1612), reaching behind (1159), hair and makeup (1202), and talking to the passenger (2568). The dataset was randomly divided into 75% for training and 25% for test data. A genetically weighted ensemble of convolutional neural networks combined with the face, hand, and skin regions was proposed to obtain an accuracy of 95.98% with the AUC dataset. [PERSON] and others [28] used the AUC dataset to achieve 96.31% accuracy through improved visual geometry group 16 (VGG-16) with regularization methods. The collection of the dataset mainly used the camera to obtain images of the driver's driving process. During the collection process, it was usually recommended that the driver perform distracting subtasks to simulate distracting driving. The distracted driving methods were mainly based on the driver's facial expression, head operation, line of sight, or body operation for feature extraction. Machine-learning methods and deep learning CNN methods were used for distracting driving recognition. However, the existing datasets and related analysis methods still encounter some problems in the research: on one hand, the current distracted driving research mainly judges driving behavior by the driver's facial and head direction, hand movements, or skin segmentation information. However, the judgment of single local information is prone to classification errors.