Driver Fatigue Detection Based On Eyelid Movement And Gaze Direction

Nowadays, many traffic accidents take place because of the fatigued drivers. Therefore, it is significant to detect drivers' fatigue state to decrease these traffic accidents. In this thesis, fatigue detection methods have been studied and driver's fatigue level has been estimated according to his eyelid movement (eye state) and gaze direction synthetically.In this thesis four facets work has been done.Firstly, in order to improve the veracity and speed of eye detection, ASM has been used for coarse eye detection and then segments the eye region alone for careful eye detection, the purpose of careful eye detection is to obtain five eye feature points. The Experiments indicate that this method is fast and accurate.Secondly, for the purpose of real time ability, this thesis proposes a novel eyes tracking method after the detecting of the eyes, which synthesizes Kalman and ASM arithmetic. The whole tracking process is separated into two steps: first of all, using Kalman to estimate the location of the eyes in the next frame according to the location in the previous frame, and then take it as the initial state of the ASM to track the eyes.Thirdly, calculates eye state and gaze direction according to the results of eye detection. This thesis proposes a novel method to get the eye state by calculating the eye black block area in segmentation image. Gaze direction has been divided into nine parts, each part can be calculated by geometrical method according to five eye feature points, which has several advantages such as briefness,celerity,better effect.Fourthly, using single parameter to estimate fatigue level doesn't work well, so this thesis combines eye status and gaze direction to create new formulas for driver fatigue detection.This thesis has three innovations:Though gaze direction was proposed before in fatigue detection, the way to calculate gaze direction didn't designate clearly. The method of image processing has been used to calculate the gaze direction and then createfatigue detection formulas with PERCLOS.This thesis proposes a novel method to combine ASM and skin color segmentation for eye detecting and tracking, which is faster and better than others.Compared with the method of using template matching to calculate eye status, the method this thesis used is more succinct and credible, which calculates the area of eye black block.