| Driver fatigue is one of the important causes in the frequent traffic accidents at present. Therefore, it is significant to monitor and prevent driver fatigue efficiently for reducing the traffic accidents.In the current driver fatigue detection field,computer vision,image processing and pattern recognition technology have been further developed and improved.The research based on driver facial features,non-contact fatigue detection algorithm and development of fatigue warning systems have become one of the mainstream.Based on the research of other people's work, I think that night is the period when driver often feel tired.So we get the positive driver images using a special infrared light and develop an effective driver fatigue detection method in accordance with the characteristics of infrared images.The whole method is divided into four processes:face detection,tracking of the human face,eye (pupilla) location and driver fatigue recognition.The study of this paper is as follows:In the first chapter, the application background, the current situation of driver fatigue research and the content of this paper are expounded.In the second chapter, Adaboost algorithm is used in human face detection and location with based on infrared conditions. Firstly, comparing the current face detection algorithms, secondly, introducing the features of infrared spectra and infrared image, and finally AdaBoost target detection algorithm is used for face detection and location. This method is efficient and effective.In the third chapter, an improved Mean-shift tracking algorithm is proposed to solve the poor tracking ability under fast-moving. When the target moves quickly, we use SSD (Sum of Square Difference) algorithm for global search. By actual face detection and face tracking as example taken, our method has advantages of quick speed and good accuracy.In the fourth chapter, there are two methods used for human eyes detection and location based on infrared conditions. (1) eyes detection and location based on restriction:MER(minimum extremal region)is introduced here to locate eye roughly;Then the natural constraints is used to filtrating the eyes step by step; Finally, an accurate eye-locating method is used to adjust the eye locations precisely,which is used for fatigue detection based on PERCLOS;(2) iris location based on Harris corner detection algorithm:Firstly, 2D maximum entropy threshold segmentation is used for segmentation of the eyes; Then Canny edge detection method is used for extracting the edge of the pupil; Finally, using Hough transform to obtain the position of the pupil, and through the Harris corner detection method to find Purkinje spots and then determine the location of the iris, which is used for fatigue detection based on Purkinje spot.In the fifth chapter, we proposed driver's fatigue detection based on infrared conditions. On the basis of precise location of the driver's eyes and the iris (Purkinje spots), respectively, the combination of PERCLOS-based measurement and Purkinje-spot are used serially for detecting the driver's fatigue, the experimental results shows that there is good accuracy.In the sixth chapter, we summarize the main content of our paper. And the objectification research of fatigue detection of driver is prospected. |
