Driver fatigue monitoring system and method based on image process and information mixing technology

Abstract

This invention discloses a system and a method for monitoring fatigue of driver based on image process and information combination technology, first of all a camera head collects continuous digital signals, tests position of the drive face and further decides positions and sizes of eye, pupil and mouth, then determines the PERCLOS, frequency of yawning, rule of head shaking, gazing direction and time of the eyes and turns the four measurement values to fatigue degree and finally uses the information combination technology to combine the information of the four characters, when his fatigue degree exceeds a set value, the alarm starts up warning.

Description

technical field [0001] The invention relates to a driver fatigue monitoring system and method based on image processing and information fusion technology. Background technique [0002] At present, there are many related researches on "driving fatigue detection" at home and abroad: "Driver Fatigue Level Detector" developed in Austria. It can accurately record the change frequency of the driver's pupil diameter for the tester to judge the driver's fatigue and the danger of driving. "Fatigue detection glasses" developed in Australia. The infrared sensor can detect the driver's eyelid activity and blinking frequency, so as to judge whether the driver is already in a state of fatigue. In addition, there are two alarms in the metal box connected to the glasses, which can remind the driver in time that "it is time to rest" and "you are already in a state of fatigue". In 1998, Zhou Peng of Shenzhen Long-distance Bus Company applied human psychology, modern neurology, and electron...

AI Technical Summary

Problems solved by technology

[0003] The above-mentioned various methods and systems at home and abroad have made great progress in monitoring and alarming systems for fatigue driving, but there are more or less the following three problems: 1. Often only a certain fatigue feature is considered

In fact, because different drivers have great individual differences, and each fatigue assessment method has different emphases, various specific methods that only consider one fatigue feature are not reliable for all drivers.

Specific detection methods that consider only one fatigue signature are even less effective when considering the complexity of environmental conditions in the cab, such as: day, night, dawn, dusk, sunlight, shadows, etc.

2. Failure to focus on fatigue detection at night

3. It is basically still in the laboratory stage and has not passed the test of the cab environment, such as: cold, heat, humidity, vibration, etc.

Images