Fatigue driving detection method integrating eye state and heart rate detection

Abstract

The invention discloses a fatigue driving detection method integrating eye state and heart rate detection, which comprises the steps of S1, acquiring real-time video of a driver through a camera, and extracting a current frame to process; S2, performing preprocessing on a current frame image, and carrying out face detection by using a Adaboost algorithm; S3, performing positioning on the eyes on the basis of a detected face, and extracting an iris; S4, calculating a PERCLOS value; S5, installing an X-CTU which is communicated with equipment through the bluetooth, and recording the heart rate; and S6, performing fatigue judgment by integrating eye state and heart rate detection. The detection accuracy of the fatigue driving detection method under a simple background is close to 100% in the face detection process; a classic PERCLOS algorithm is improved, and the function can be realized through coarse positioning for an eye region without a need of knowing the exact position of the eyes or calculating the closure degree of the eyes; two parameters of eye state and heart rate detection are integrated, and both the detection accuracy and the real-time performance are improved.

Description

technical field [0001] The invention relates to a fatigue driving detection method which combines eye state and heart rate detection. Background technique [0002] With the continuous and rapid development of the transportation industry and manufacturing industry, the number of cars has increased year after year, and has become one of the most important means of transportation for daily travel. The resulting traffic accidents have attracted the attention of more and more countries and institutions. There are many reasons for traffic accidents. For example, drunk driving can be detected through relevant means, such as blood alcohol concentration detection, which has obvious characteristics and is easy to identify, while fatigue driving is relatively hidden and cannot be directly judged. [0003] According to the statistics of the traffic department, the number of accident deaths caused by fatigue driving accounts for 15.1% of the death toll in traffic accidents. Fatigue drivi...

AI Technical Summary

Problems solved by technology

There are many reasons for traffic accidents. For example, drunk driving can be detected through relevant means, such as blood alcohol concentration detection, which has obvious characteristics and is easy to identify, while fatigue driving is relatively hidden and cannot be directly judged.

[0003] According to the statistics of the traffic department, the number of accident deaths caused by fatigue driving accounts for 15.1% of the death toll in traffic accidents. Fatigue driving is very harmful and is one of the main hidden dangers that seriously affect traffic safety. Currently, research on fatigue driving has a single signal source. Low recognition accuracy and poor practical performance

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