Fatigue driving detection and early warning system based on machine vision

Abstract

The invention discloses a fatigue driving detection and early warning system based on machine vision. The system comprises a driving duration obtaining module, a central processing module, a human face video image acquisition module and an early warning module; the driving duration obtaining module, the human face video image acquisition module and the early warning module are all connected with the central processing module; the driving duration acquisition module is used for acquiring a driving duration of a driver and sending the driving duration to the central processing module; the central processing module is used for judging a driving state of the driver according to the driving duration of the driver, and driving the human face video image acquisition module to acquire a face video image of the driver when the driver is judged to be in a fatigue driving state; and the central processing module receives the face video image, judges whether the driver is in the fatigue driving state or not according to the received face video image, and if the driver is judged to be in the fatigue driving state, driving the early warning module to send out an alarm. According to the system, the fatigue detection is carried out in a non-contact mode, so that the use by the driver is more convenient.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to a fatigue driving detection and early warning system and method based on machine vision. Background technique [0002] With the rapid development of my country's economy and society and the increase of national income, more and more cars have gradually entered the homes of ordinary people. While cars bring efficiency, convenience and comfort to our lives, they also bring us the trouble of traffic accidents. According to the statistics of my country's traffic department, traffic accidents caused by fatigue driving account for about 20% of total traffic accidents, 40% of serious traffic accidents and 83% of traffic fatalities. [0003] In the prior art, by wearing an earphone with gravity sensor on the driver's ear, it is detected whether the driver has a head-down phenomenon, and then it is judged whether the driver has fatigue driving. However, this fatigue detection metho...

AI Technical Summary

Problems solved by technology

However, this fatigue detection method is often not accurate enough, and it is easy to cause misjudgment. For example, when the driver just lowers his head to check things or check things on the car, or other lowering actions, it will be triggered by mistake.

Furthermore, wearing the earphone for a long time can easily cause the driver's discomfort and make the driver feel repulsive.

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