Research on key technologies of intelligent transportation based on image recognition and anti-fatigue driving

• DOI: 10.1186/s13640-018-0403-6
• Publication Date: 2019-02-04T12:03:23Z
• Authors: Jun Wang; Xiaoping Yu; Qiang Liu; Zhou Yang
• Keywords: Fatigue driving; TK7800-8360; 11. Sustainability; 0202 electrical engineering, electronic engineering, information engineering; Image recognition; 02 engineering and technology; Electronics; Intelligent traffic

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.