Rail traffic driver fatigue state monitoring method

Abstract

The invention discloses a rail traffic driver fatigue state monitoring method. Through collecting the head neck image of a driver, the position of a face is determined, the facial expression image of the driver is collected according to the face position, and the positions of eyes and a mouth are determined; Left and right eye state characteristics and a mouth expression characteristic are recorded and are transmitted to a state identification unit, the characteristics are classified by a classification algorithm, and whether the driver is in a drowsy state or not is judged; According to the face position, a coordinate system is established at the face, a coordinate system movement characteristic and the head and neck movement characteristic in a human body drowsy state are compared, and thus whether the river is in a drowsy state or not is judged; and if a condition that the driver is in the drowsy state is detected, the alarm device emits an alarm prompt. The method has the advantages of a simple and efficient system design, high detection precision, low requirements of image processing and corresponding hardware, low cost, small area occupation, and strong robustness, the fast and correct fatigue driving judgment can be realized, the detection sensitivity is high, and the method is safe and reliable.

Description

technical field [0001] The invention relates to the technical field of automatic video detection, in particular to a method for monitoring fatigue status of rail transit drivers. Background technique [0002] In recent years, the construction speed and operation scale of my country's high-grade railways have reached the world's leading level. As the number and course of train operations increase year by year, the average working hours of train crews also increases. The human factor of the train driver is the most critical and noteworthy among the potential safety hazards of train operation. Compared with road transport, research and analysis by scientists in the United Kingdom and the United States shows that road traffic accidents caused by drivers themselves account for 57% to 65% of the total number of traffic accidents, and accidents caused by factors related to drivers account for nearly 95% of the total number; The statistics of road traffic accidents in my country al...

AI Technical Summary

Problems solved by technology

However, due to the large individual differences in the characteristics of human eye movements, and the strong randomness of the movements, the detection accuracy is not satisfactory, and when collecting images from the front of the human body, it is difficult to detect the vertical movement of the eyes and head and neck from the images. detected in the background, which further affects the accuracy of the detection

[0005] In summary, various fatigue driving detection methods in the prior art have problems such as poor detection accuracy, high image processing requirements, high technical difficulty, and high cost of software and hardware design and implementation.

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