Driver fatigue and distraction during travel are the major causes for the road accidents. Many driver monitoring systems have been proposed in recent years for monitoring driver activities to avoid accidents. Most of the existing systems are in the form of specialized embedded hardware, majorly present in luxurious vehicles. This paper presents an effective driver fatigue and distraction monitoring system for Android Automobiles. An intelligent system for monitoring driver fatigue and distraction during travel using Adaptive Template Matching and Adaptive Boosting is designed and implemented here. A novel approach of detecting eye rub due to irritation in eye and yawning detection through intensity sum of facial region is also proposed. Experiments are conducted using android OpenCV which can be installed in low cost smartphones as well as in Android Auto. Experiment results shows that a high accuracy of driver distraction is detected in different vehicles and camera locations .CodeShoppy
Eye rubs or squeezes are one of the major reasons for accidents. Even a skilled driver is distracted by irritation in eye caused by external factors like dust. This reduces the concentration of the driver on road and may lead to an accident. The system proposed here is designed in a way to detect these kinds of distractions. When such distractions are found, the system will immediately alert the driver with some possible suggestions through voice alert. Eye rubs are detected by the difference in the black pixels of the eye regions ERP = RR – RL where ERP is the Eye Rub Percentage, RR is the Ratio of black pixels in Right eye and RL is the Ratio of black pixels in the Left eye. If the value of ERP is greater than 5% then it is considered as eye rub and driver will be alerted with recorded voice suggestions. In Figure 3, the A and C show the various rub positions and B and D shows the corresponding binary mage of it. Eyes are detected separately using Haar like feature. The sum of the black pixels in the left eye is lesser than the sum of the black pixels in the right eye. This will ring the alarm and provide suggestions to the driver through voice alert.
Haar like feature detector is used to detect the mouth. To increase the computational efficiency, the lower region of the face alone searched for mouth. Adaptive template matching and adaptive boosting is used to detect the mouth even when the face is rotated. Yawning Detection Yawning is not only a sign of tiredness but also a sign of changing body conditions. Repeated Yawning during travel is an indication of driver fatigue. Using the change of black pixels in mouth region yawning is detected. An open mouth is often darker than the closed mouth. Thus an open moth should have more black pixels compared to the closed. In the detected mouth region, the image is converted into binary image as shown in the Figure 4. Compared to the B, Dhas more black pixels which indicate the open mouth. It is important to not misclassify a partial open mouth as yawn. So a threshold value is fixed for Yawning and number of yawn count is logged. The Fatigue detection system will analyze the yawn counts and eye blinks to make the final decision. If the number of yawn count exceeds 3 then the driver is alerted with recorded voice
Boon Giin Lee et al  proposed a method to monitor driver safety by analyzing information related to fatigue using eye movement monitoring and bio signal processing. It is implemented using a smartphone and sensors are embedded through bluetooth. But the processing time is slow because of the native camera feature. Boon Giin Lee enhanced the  and proposed a new system in  which combined various bio sensors for effective monitoring of driver distraction and fatigue. To indicate the driver capability level Fuzzy Bayesian framework is designed. But the Processing speed is slow because of the limited resource and driver alerting mechanism is not effective