The Design And Implementation Of Driving Fatigue Detection System Based On Human Facial Features

As we all know, driving fatigue has becoming another important reason of global accidents. At the same time,it brings traffic safety risks and gets increasingly widespread public concern. Using scientific testing methods canassist safe-driving and prevent driving fatigue is hot topic of current researches. In this thesis, a video-basedhuman facial features state analysis to determine whether a state of fatigue driving. Specific work carried out asfollows:First, face detection. Adopt by trained frontal face sample classification approach for real-time detection ofhuman faces. Second, face tracking. Come up with the face tracking strategies of TLD (Tracking-Learning andDetection) which was used to tracking target in case of some special situations. Third, use establishedmathematical model to analysis human eyes and mouse’s states. In order to make the model more accurate, we usethe variable control method to make the series of data sets to feedback and update mathematical model. Thismethod can effectively determine the real-time status of the human facial feature. At last, a new discriminativemethod of fatigue based on multi-feature fusion was presented, which was not only based on the P80model ofPERCLOS algorithm, but also with the features of human mouse.All in all, the thesis focus on the research of fatigue driving detection based on human facial features andproposed face detection and face tracking method to get an accurate combination of face, and use modeling anddemonstration to analysis the face feature. Finally, the integration of multi-feature information to determinewhether the driver is driving under fatigue.