Eye tracking prediction based on uniformly variable of physics thought

摘要

On the basis of researching the current situation of fatigue driving and reference the existing literature both at home and abroad, this thesis systemically put forward concerning fatigue detection's overall design scheme. Firstly, in the detection stage, face detection was implemented based on the face Adaboost cascade classifier, and the driver's face was found after judging all the faces, then eye detection was implemented based on the eye Adaboost cascade classifier in driver's face region. Secondly, in the eye tracking stage, with the thought of uniformly variable, eye regions were predicted through the displacement of the three frames before, and then template matching was used in these regions. Test has been done on real-time and accuracy under different prediction method, light, movement speed, and rotation angles. The test result indicates that in the normal sunlight and the pixel-level video, the driver's detection algorithms introduced by this thesis can be precisely implemented on time in every process, and can accurately reflect the driver's fatigue.