Research And Implementation Of The Core Algorithm In Driver Fatigue Monitoring System

Driver fatigue is an important cause of traffic accidents. In order to reduce traffic accidents caused by driver fatigue, we can manage to promptly alert the driver when the driver enters fatigue state. In order to achieve this goal we need a real-time and accurate driver fatigue monitoring system. Until now, people have proposed a lot of driving fatigue monitoring methods. In various ways, the monitoring algorithm based on image processing is an important class. But because of the complexity of human faces, and the complexity of the external environment, all these methods based on image processing algorithms still have a lot of problems in real-time and robustness.Face landmarking algorithms are important and fundamental algorithms to task focused on face. There are already many applications, such as face recognition and facial expression recognition. But the application in driver fatigue monitoring is less and existed application is not sufficient. Therefore, we will study and implement driver fatigue monitoring system with face landmarking algorithm as the core. Then research and implemente other three sub-modules of the core algorithm in driver fatigue monitoring system. The main research work of this paper includes the following aspects:(1) Produce a particular database for modeling and testing. Because all of the face database with artificial ground-truth landmarks lack fatigue state face images, we integrate some existing databases and add additional collection of face images to produce a particular database for driver fatigue monitoring.(2) Suggest a landmarking algorithm with multi-model for obtaining fatigue-related imformation. The paper introduces the fundamentals of ASM, AAM, STASM, CLM, the four popular landmarking algorithms. We have experiments to compare these four algorithms, mainly to obtain two key perspectives as the perspective of human face location and of status information in partial face. We compare four different face landmarking algorithms’ ability by experiment them on different face landmarks sets. Then we analyze and summarize the performance characteristics of these algorithms.(3) Four sub-module systems of the driver fatigue monitoring system are designed in this paper: face detection, image enhancement, face landmaking and fatigue judgment. Face detection module uses face detection algorithm based on Ada Boost as the core. Image enhancement module is mainly to remove the uneven illumination. Fatigue judgment module, for example, use PERCLOS as the criteria of fatigue.(4) According to the needs and characteristics of driver fatigue monitoring, this paper optimize these four sub-modules, then combines the various sub-module as a whole. Especially, by taking advantage of face landmarking algorithms tracking capability, we ensure the accuracy as the premise of the algorithm while improved the speed of the algorithm. We realized the entire core algorithm, had simulated experiments on it. From the experimental results, the system has a good performance.