Eye State Tracking Under Driving Conditions

Fatigue driving is currently one of the leading causes of car accidents. The driver's fatigue detection system is designed for the real-time monitoring of drivers, to avoid accidents it will alarm the driver, when he is drowsing. Study of these products has been the concern of the world's major automotive manufacturers. The fatigue detecting method based on PERCLOS (percentage of eyelid closure) is the most practical and reliable in a variety of methods of fatigue detection. The key of this method is real-time eye state tracking under driving conditions.Eye state detection is a complex classification problem. This paper presents a novel algorithm of eye state detection. This method is simpler than traditional method; first it detects face from the input image, gets the results of eye state by directly detecting open eyes on face images.Face detection and open eye detection is two main algorithm of this article. In essence, they are target object detection on the input images. This article proposes a new feature classifier training method based on detailed analysis of the feature classifier architecture of Viola face detection. In the research, after the incremental iterative training of collected ruminant sample sets of open eye images, we've got an excellent classifier of open eye image detection classifier. Open eye image detection accuracy of the classifier reaches 94.62%.The speed and accuracy of eye state detection algorithm are two difficulty problems, Previous algorithm is difficult to have both speed and accuracy. The proposed algorithm makes full use of face image information, and completes eye state identification simply and accurately by the classifier of open eyes image detection. From the tested results in this paper, completing eye state identification on 960 color images(320 * 240Resolution) took an average processing time of 18.8 ms, with identification accuracy rate of 98.12% and frame speed up to 53 frames per second. This shows that the proposed eye state detection algorithm can achieves high accuracy and speed.Finally, source code implementation of eye state tracking algorithm is completed, which can be compiled cross-operating system.