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Android Open CV Based Effective Driver Fatigue

Android Open CV Based Effective Driver Fatigue

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

Road safety is one of the main objectives in designing driver assistance systems. On average, every 30 s, one person dies somewhere in the world due to a car crash. The cost of accidents in the United States is estimated to be about $300 billion annually [1], i.e., about 2% of its gross domestic product. Conservative estimates suggest that a high proportion of fatalities and injuries due to traffic accidents involve impaired drivers. It is projected that these figures could be increased by 65% in the next 20 years, unless novel driving risk reduction methods are leveraged [2]. Among all fatal traffic accidents, 95% are caused by human errors [3]. The three major causes of these human errors, which are often referred to as the “Big Three,” are alcohol, drowsiness, and inattention [4]. Statistics show that 25% of fatal accidents in Europe [5], 32% in the United States [6], and 38% in Canada [7] are caused by drunk drivers. The National Highway Traffic Safety Administration (NHTSA) reports that over 3000 fatalities from automobile accidents are caused by drowsiness or distraction [8]. Drowsiness is common during travel. Driver will either resist the drowsiness or stop the vehicle aside and continue the journey after a while. But sometimes driver may be unaware that they are actually feeling fatigue and it is the cause for major accidents. To avoid this, first driver fatigue has to be identified and alerted. Some external factors like dusts can cause irritation in eye and divert a driver during travel. Even an experienced driver can also be diverted from driving which eventually leads to an accident. So driver distractions during travel also have to be identified and alerted. Google designed an android based automobile operating system for cars which will be available in markets by middle of 2015. This operating system will be available in cars across 24 top and medium level car manufacturers in name of Android Auto. With a simple and intuitive interface, integrated steering wheel controls, and powerful new voice actions, it’s designed to minimize distraction so driver can stay focused on the road [9]. So it is wise to propose a system which can be installed as software instead of an external hardware.

 Android Open CV Based Effective Driver Fatigue

This paper presents an effective driver fatigue monitoring system for Android Automobiles. In this work various driver monitoring methods are explored for Android Automobiles. An intelligent system for monitoring driver distraction and fatigue during travel using adaptive template matching and adaptive boosting is designed and implemented. A novel approach of detecting eye rub due to irritation in eye and yawning through intensity sum of facial region is also proposed here. Experiments are conducted using low cost xiaomi redmi 1s smartphone powered by 1.6GHz processor with 1GB RAM. The application can be easily installed in Android Auto with small modifications. Experiment results shows that a high accuracy of driver distraction is detected in different scenarios with different vehicles and camera locations. Preprocessing of frames is done to avoid noise obtained during video acquisition

Many driver monitoring systems have been proposed in recent years for monitoring driver activities to avoid accidents. Vision based intelligent system through smartphones offers rich potential for application development as well as research. Jaeik Jo et al [10] proposed four new methods to identify driver distraction and drowsiness. First driver drowsiness is identified. Second eye detection algorithm is proposed to avoidunclear image due to reflection in glasses. Third eye detection accuracy is enhanced by applying eye validation after initial detection of eye. Fourth a novel eye state detection algorithm is proposed. Though it has innovative approaches a laptop is required to process the frames

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