Research On Fatigue Driving Detection Method Based On Internet Of Things Cloud Platform

In today’s society,road traffic safety has become one of the issues that people are paying more and more attention to,and fatigue driving is one of the main causes of traffic safety accidents.Traditional fatigue driving detection focuses on the facial features of the driver,but it is difficult to reach an ideal level in detection accuracy and detection speed.At the same time,traditional fatigue driving detection does not have effective early warning measures,but only pays attention to early warning drivers.Under certain circumstances,when the driver enters a state of fatigue,the state of consciousness is poor,which makes it impossible to respond to the fatigue warning in time,and it is difficult to avoid traffic accidents.Therefore,how to realize the fatigue detection of the driver in terms of speed and accuracy,and at the same time,to alert the driver in time and allow the driver to take timely measures to be an important challenge to be solved.In order to solve the above-mentioned problems,this paper innovatively proposes a fatigue driving detection method based on the Io T cloud platform.The main contributions are as follows:1.Propose a remote extraction algorithm for the driver’s heart rate feature based on the facial feature point model.For the face image,68 facial feature point coordinates are constructed based on the deep learning algorithm,and then the face feature point model is constructed.Based on the subtle changes in facial skin characteristics,the r PPG algorithm improved by the chromaticity method is used to propose an improved method of remote heart rate detection algorithm based on specific areas of interest on the face,which achieves the physiological level of non-contact heart rate detection for fatigue driving.2.Propose a comprehensive fatigue driving detection model based on the multi-feature fusion constructed based on the driver’s facial motion features,head posture estimation,and facial microfeature model.Calculate the OPEN-RATE of the eyes and mouth opening and closing ratio based on the facial feature point model,construct the facial feature time series based on the video stream and calculate the driver fatigue state? at the same time construct the driver’s head spatial feature model,and calculate the head based on the spatial feature model.Department posture estimation.Combining with non-contact remote heart rate measurement,it realizes multi-faceted feature detection of driver fatigue.3.A cloud-end collaborative fatigue driving early warning model is constructed based on the intelligent Io T cloud platform to realize driver fatigue driving monitoring in smart traffic scenarios.A framework for fatigue driving detection and early warning based on the Io T cloud platform is built.Based on the visualization development platform,the visualization analysis and monitoring of the driver’s facial and physiological characteristics data are realized.This paper has conducted research and experiments on multiple public data sets to prove the effectiveness of the method and prove that the method can detect the fatigue state of the driver in time and achieve better results.At the same time,the One NET cloud platform is adopted.A simulation test is carried out to prove that this method can achieve the ideal driver fatigue driving data monitoring effect,and meet certain application requirements of actual scenes.