Abstract
Intelligent transportation system needs to solve the main problems in traffic safety. This paper focuses on the traffic safety caused by fatigue driving based on image recognition of key technologies for research and analysis. This paper proposes that the location of face and facial feature points and the classification of fatigue detection are the key links to determine the fatigue driving detection rate. In the analysis of face localization algorithm based on skin color modeling, a corner-based optimization method is proposed to optimize the face region. Based on the analysis of the binary algorithm of human eye localization algorithm, a bi-directional integral projection method is proposed to achieve accurate human eye localization. Then the commonly used fatigue classification algorithm (KNN algorithm) is analyzed. Finally, the proposed method is verified by the simulation test of fatigue driving. Experimental results show that the algorithm based on skin color modeling can accurately locate the driver’s face region. The eye location algorithm based on the two-valued algorithm can also locate the eye location of the tester accurately. The accuracy of KNN fatigue detection model is 87.82%. It can identify driver’s fatigue state with high accuracy.
Highlights
At the same time of large-scale urban expansion, the reform of infrastructure construction and management mode is relatively lagging behind, resulting in “urban disease” becoming more and more serious
Traffic safety is one of the main problems in the development of urban transportation, and it needs to be solved in time [2]
Fatigue driving early warning technology based on image recognition theory is the key technology widely used in Intelligent transportation system (ITS)
Summary
At the same time of large-scale urban expansion, the reform of infrastructure construction and management mode is relatively lagging behind, resulting in “urban disease” becoming more and more serious. The explosive growth of urban population and the rapid increase of the number of vehicles in the city have led to urban traffic obstacles and development bottlenecks. The main obstacles and problems are as follows [1]: serious urban traffic congestion, resulting in increased travel time and consumption of large amounts of energy, serious traffic safety problems, and frequent accidents; noise pollution and air pollution are becoming increasingly serious. Traffic safety is one of the main problems in the development of urban transportation, and it needs to be solved in time [2]. Statistics show that in 2016, 86.443 million traffic accidents occurred in
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