# Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network
## 1 Introduction  
According to the World Health Organization (WHO) global status report on road safety, in 2018, traffic accidents were the eighth leading cause of death in the world [1]. Nearly 135 million people were killed and injured every year. According to the report, nearly one-fifth of the traffic accidents were cause by driver distractions [2]. Although the mortality rate of traffic accidents in the world declined in recent years, compared with other countries, the mortality rate is much higher in Taiwan. In addition, due to the development of science and technology in recent years, in-vehicle information systems (IVISs) such as navigation and media devices were installed in cars. These devices would introduce more driver distractions and lead to more accidents [2]. Road traffic accidents cause huge damage, and the number of accidents due to distractions is increasing. According to the National Highway Traffic Safety Administrator of the United States (NHTSA) reports [2], human errors caused approximately 90% road accidents in the United States and represented a dominant factor for vehicle crashes [3]. Among them, the major cause of these accidents was the use of mobile phones [4]. As a result, the government enacted several laws to punish drivers and prevent distractions caused by the use of hi-tech products while driving. In fact, using a smartphone is not the only reason for driver distractions. NHTSA defines a distraction during driving as \"any activity that diverts attention of the driver from the task of driving\", including talking or texting on one's phone, eating and drinking, or talking to people in the vehicle [4]. The Centers for Disease Control and Prevention (CDC) [5] classified distracted driving as cognitive,visual, and manual distractions. Cognitive distraction means that the driver's mind is off driving. In other words, even though the driver is in a safe driving posture, they might be lost in their thoughts and mentally distracted from safe driving. Visual distractions refer to situations where the driver's eyes are off the road because of fatigue, sleepiness, drowsiness, inattention, or the use of multimedia devices. Manual distractions are concerned with various activities where the driver's hands are off the driving wheel. Such distractions include using the cellphone, eating or drinking, adjusting hair and makeup, or talking to a passenger. In order to prevent accidents, distraction detection systems become an important component in semi-autonomous or autonomous cars, which can alert the driver to potential problems. Motivated by the fact that the major cause of manual distractions is the usage of the cellphones [6], some researches focused on cellphone usage detection while driving. In 2011, [PERSON] et al. [7] extracted features of the face, mouth, and hands from the images captured by a camera installed on the dashboard, and a hidden condition random field (HCRF) model was applied to detect cellphone usage. In 2014, [PERSON] et al. [8] applied an support vector machine (SVM) model to check the hand and face locations and detect the usage of cellphones with a frontal image view of the driver. In 2015, [PERSON] et al. [9] used AdaBoost and hidden Markov models to classify driver distraction by analyzing the RGB-D data captured by Kinect sensors. However, in the data collection process, the experimental set-up missed two essential points: the lighting conditions and the distance between the sensor and the driver [2]. In real cases, a driver is exposed to a variety of lighting conditions, including sunlight and shadows [2]. [PERSON] et al. [10] created their own dataset for cellphone detection and applied a supervised descent method (SDM) to track the locations of face landmarks for the extraction of regions of interest. Unlike previous methods, no assumption where a face or hands were expected to be found was made.