# A Data Augmentation Approach to Distracted Driving Detection
## 1 Introduction  
According to the World Health Organization (WHO) global status report [1], road traffic accidents cause 1.35 million deaths each year. This is nearly 3700 people dying on the world's roads every day. The most heart-breaking statistic is that road traffic injury has become the leading cause of death among people aged 5 to 29 [2]. The investigation [3] for the cause of car collisions shows that 94% of road traffic accidents in the United States are caused by human operations and errors. Among them, distracted driving, which can reduce the driver's reaction speed, is the most dangerous behavior. In 2018 alone, 2841 people died in traffic collisions on United States roads due to driver distraction [4]. The impacts of distracted behavior of drivers are multifaceted [5], including visual behavior, operating behavior, driving pressure, and the ability to perceive danger, etc. According to the definition of the National Highway Traffic Safety Administration (NHTSA) [6], distracted driving refers to any activity that can divert attention away from driving, including (a) talking or texting on a phone, (b) eating and drinking, (c) talking to others in the vehicle, or (d) using radio, entertainment or navigation system. Distracted driving detection can be used to give early warning of dangerous driving behavior, including using a mobile phone to call or send text messages, using navigation applications or choosing to play music, etc. [7]. Distracted driving detection methods are mainly based on the driver's facial expression, head operation, line of sight or body operation [8]. Through visual tracking, target detection, motion recognition and other technologies, the driver's driving behavior and physiological state can be detected. In the early days, researchers mainly focused on behavior analysis based on the driver's line-of-sight direction through the eye, face, and head operation. In 2002, [PERSON] et al. [9] proposed a method of tracking the driver's facial area and used the yaw direction angle to estimate the driver's facial operation to detect the driver's facial orientation. [PERSON] et al. [10] successfully developed a driver's facial operation detection system based on binocular stereo vision, using hidden Markov models to predict the driver's facial operation in 2007. Later, with the development of machine learning technology and the public of driving behavior datasets, increasing studies were added to analyze the driver's phone calling, drinking, eating and other unsafe driving behaviors. Southeast University driving posture (SUE-DP) dataset [11] was proposed in 2011. The experiment collected four types of distracted driving behaviors: \"grasping the steering wheel\", \"operating the shift lever\", \"eating,\" and \"talking on a cellular phone\". A series of studies have been conducted based on the SUE-DP dataset: [PERSON] [12] used the multiwavelet transform method and the multilayer perceptron (MLP) classifier to recognize four predefined driving postures and obtained an accuracy of 90.61%. [PERSON] et al. [11] introduced a contourlet transform method for feature extraction and achieved 90.63% classification accuracy. Subsequently. [PERSON] [13] used support vector machines (SVM) algorithm with an intersection kernel for obtaining 94.25% accuracy. In 2013, The image pyramid histogram of oriented gradients (PHOG) features and edge features [14] were extracted comprehensively to increase the classification accuracy to 94.75% within MLP. In 2014, [PERSON] [15] extracted the PHOG features of historical moving images containing time information and obtained 96.56% accuracy through the random forests (RF) algorithm. Recently, with the development of deep learning classification and detection technology, increasing researchers analyzed driving behavior through convolutional neural networks (CNNs). More researchers have also begun to build their own research datasets.