# Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network
## 2 Related Works  
In the computer vision field, action recognition was developed for many years. Among these researches, how to extract action features is a key issue. In early years, studies focused on the hand-crafted local features that were designed by human experts to extract a given set of chosen characteristics [29]. Since temporal information includes important cues for action recognition, famous feature descriptors for image classification such as scale-invariant feature transform (SIFT) and histogram of oriented gradient (HOG) were extended to extract features for 3D data (2D spatial + one-dimensional (1D) temporal data). In 2003, [PERSON] et al. [30] proposed spatio-temporal interest points (STIPs) by extending Harris corner detectors. SIFT and HOG were also extended to SIFT-3D and HOG3D for action recognition. [PERSON] et al. [31] proposed the cuboid feature for behavior identification. [PERSON] and [PERSON] [32] established the ActionBank for action recognition. In 2013, [PERSON] et al. [33] proposed improved dense trajectories (iDT). Although action recognition was studied for at least 20 years, the performance is still limited due to the difficulties including large appearance and pose variations, as well as a cluttered background [34]. With the development of convolutional neural networks (CNNs), CNN showed the advantages of performance improvement for action recognition compared to hand-crafted features [29]. It also makes real-life applications of action recognition possible. Among them, driver behavior analysis is a specific issue and becomes very important because in-vehicle information systems (IVISs) such as navigation and media devices are being developed rapidly but they introduce more distractions which cause accidents. In early studies of driver distraction detection, motivated by the fact that the major cause of distractions is using a cellphone while driving [6], the detection of cellphone usage became a focus and many machine learning models were applied. For example, in 2011, [PERSON] et al. [7] extracted the features from the image captured by a camera installed on the dashboard and a hidden condition random field model was applied. [PERSON] et al. [8] applied an SVM model to check the hands and face locations with the frontal image view of the driver. In order to cope with occlusion, [PERSON] et al. [9] used the RGB-D data captured by Kinect sensors, but the distance between the sensor and the driver was missed in the data collection process. Unlike previous methods that assumed safe driving based on the location or state of the face or hands, [PERSON] et al. [10] applied a supervised descent method to track the locations of face landmarks, and an AdaBoost classifier trained for each side of the face regions was used to classify the cellphone usage as right hand, left hand, or no usage. According to the definition of a distraction by the NHTSA as \"any activity that diverts attention of the driver from the task of driving\" [4], the CDC [5] classified distracted driving as cognitive, visual, and manual distractions, whereby not only cellphone usage detection was developed but more distracted behaviors were also considered. In 2014, [PERSON] et al. [35] collaborated with the University of California, San Diego (UCSD) Laboratory of Intelligent and Safe Automobiles and presented a vision-based hand activity analysis system to detect three types of distractions: adjusting video, adjusting mirrors, and operating gear. The images are captured by two Kinect cameras that can provide the frontal and back views of the driver. [PERSON] et al. [36] divided images into three regions: steering wheel, gear, and radio panel. A region-based model was designed to detect the presence of hands in certain pre-defined regions to classify three types of distractions. A more inclusive distracted driving dataset including four distractions (safe driving (holding steering wheel), operating shift, eating, and talking on the cellphone) was considered in Reference [37]. [PERSON] et al.