Fatigue driving detection method and system based on driver's head and neck movement feature recognition

Abstract

The invention discloses a method and a system for detecting fatigue driving based on head and neck movement feature recognition of a driver. The method comprises the following steps: (1) arranging a camera on the side face of a driving position in a car, and collecting images of the head and neck part of the driver from the side face; (2) storing a group of images collected by the camera in the step (1) through an image processing unit at intervals of time T, and abstracting contour lines of head and neck side images of the driver; and (3) forming head and neck movement features of the driver according to a plurality of groups of continuous contour lines of the head and neck side images of the driver, wherein the continuous contour lines of the head and neck side images of the driver are obtained from the step(2), comparing the head and neck movement features of the driver with the head and neck movement features when a human body is in a sleepy state, carrying out judgment, if the head and neck movement features of the driver conform to sleepy state requirements, considering the driver to be fatigue driving and sending an alarm prompt, or else, repeating the step (2), and keeping operation continuously and circularly. The method and the system for detecting fatigue driving based on head and neck movement feature recognition of the driver have the advantages that detecting recognition accuracy is high, the requirements for an image processing algorithm and corresponding hardware are low, and implementation is easy.

Description

technical field [0001] The invention relates to a fatigue driving detection method and system, in particular to a fatigue driving detection method and system based on driver head and neck movement feature recognition. Background technique [0002] With the continuous increase of the total highway mileage and the number of motor vehicles, and the increase of vehicle speed, road traffic accidents in my country are increasing year by year, and the number of major accidents and casualties is on the rise. It has become a threat to the safety of people's lives and property and the sustainable development of society. Serious Problem. In many traffic accidents, the driver's human factor has become one of the main factors of traffic accidents. 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 rela...

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] There is also a fatigue detection system based on human head posture recognition in the prior art. For example, CN201120266804.8 discloses a posture recognition-based anti-fatigue driving assistance system, which uses a camera with an infrared light source to collect head posture to complete recognition of fatigue driving. , the cameras are distributed on the left and right sides in front of the face and face at an angle of incidence of 45°, the two cameras collect the user's facial image information, input it to the information processing device, and use software to process the driver's facial image , calculate the 3D posture of the head, and then judge the basic driving actions of the driver, such as looking straight ahead, looking left, looking right, looking up, looking down, etc. However, this method also collects the frontal image of the human face. The difference in skin color is small, so when using the front view of the human face to identify head posture changes, it is necessary to extract the parts with special colors such as eyes and mouth from the face for recognition processing. The requirements for image processing are very high, and the technology is difficult. High cost of hardware design and implementation

[0006] In the prior art, there are also physiological information detection methods for fatigue driving detection. For example, although the common fatigue driving detection method based on EEG method has high detection accuracy, it needs to stick electrodes on the head during measurement. This kind of intrusion The type test is not popular with drivers; moreover, the fatigue driving detection method based on EEG is easily disturbed by the external magnetic field during the detection process, which limits its use.

[0007] In summary, various fatigue driving detection methods in the prior art have defects such as poor detection accuracy; high image processing requirements, high technical difficulty, high cost of software and hardware design and implementation; or the need for intrusive detection of drivers.

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