## 2 Related Work
proposed a driver activity recognition system based on deep convolutional neural networks and reported to achieve an accuracy of 81.6% using AlexNet, 78.6% using GoogleNet, and 74.90% using ResNet50 neural networks [51]. [PERSON] et al. proposed a two-stream CNN model to estimate the spatial and temporal parameters of driver behavior [52]. The authors reported an increase in accuracy of 30% compared to the score-level fusion neural network model. In multimodal head pose estimation, gaze tracking is often used to improve the head pose estimation result. For example, [PERSON] et al. proposed a hybrid scheme to combine eye location and head pose information to yield better gaze estimation [53]. The information obtained from the head pose was utilized for normalizing the eye regions, while the information generated by the eye location was used for correcting the pose estimation procedure. The experimental results indicated that the combined gaze estimation system was accurate with a mean error of 2-5\({}^{\circ}\). [PERSON] et al. studied a gaze tracking system that was important for monitoring driver's attention, detecting fatigue, and providing better driver's assistance systems, but it was difficult to deploy due to large head movements and highly variable illumination [54]. The authors proposed a calibration method for determining the head orientation of the driver that utilized the rear-view mirror, the side mirrors, and the instrument board as calibration points. The system categorized the head pose in twelve gaze zones based on facial features using a self-learning algorithm. Experimental results showed that the automatic calibration method achieved an MAR of 2.44\({}^{\circ}\) in yaw estimation and an MAR of 4.73\({}^{\circ}\) in pitch estimation during day and night driving. [PERSON] et al. described a vision-based system to detect Eyes Off the Road (EOR) distraction [55]. The system had three components: head pose and gaze estimation, robust facial feature tracking, and 3D geometric reasoning to detect EOR distraction. Experimental evaluation under a wide variety of illumination conditions, facial expressions, and individuals showed that the system achieved above 90% EOR accuracy for all tested scenarios. [PERSON] et al. proposed a data mining approach for comparing the neutral driving state with the cognitive distracted state by monitoring the vehicle behavior and driver's gaze variations [56]. The proposed method achieved a classification accuracy of 96.2% under the distracted condition and 76.6% under neutral condition. [PERSON] et al. investigated the question: How much better could the driver gaze be classified using both eye and head pose versus only head pose [57]? The experimental results showed that eye pose increased the average accuracy from 89.2% to 94.6%. [PERSON] et al. proposed a method that relied on fuzzy-system for detecting a driver's corneal and pupil specular reflection (SR) that could track the gaze in a vehicle environment [58]. Based on the fuzzy output, the proposed method excluded the eye region that had a high error rate. Experimental results on 20,654 images showed that the method achieved a mean pupil detection error of 4.06 pixels and a mean corneal SR detection error of 2.48 pixels across different gaze regions. Additionally, information obtained from external car monitoring can be used. For example, [PERSON] et al. emphasized that major advancements in driver distraction could be achieved by jointly analyzing and fusing the internal state of the vehicle and the external state of the vehicle [59]. The authors pointed out that in the CARSAFE mobile application, 83% precision and 75% recall rate for dangerous driving situations were achieved by combining both the internal video monitoring of vehicle and external video monitoring. [PERSON] et al. proposed using deep learning-based classification (DarNet) on driver image data and inertial measurement unit data, attaining an accuracy of 87.02% [60]. [PERSON] et al.