Mingwu Ren

Driver Fatigue Detection: A Survey

Driver fatigue is an important factor in a large number of accidents. There has been much work done in driver fatigue detection. This paper presents a comprehensive survey of research on driver fatigue detection and provides structural categories for the methods which have been proposed. The methods of fatigue detection mainly focused on measures of the drivers state, driver performance and the combination of the drivers state and performance. The measures of drivers state included PERCLOS, mouth shape and head position; the measures of driver performance included lane tracking and tracking of distance between vehicles. These approaches are presented and discussed in detail. Some typical driver monitoring systems are also introduced in this paper. Finally, summary and conclusions are presented

Road detection based on shape prior, color representation and discrimination analysis

Vision based road detection is a key technique in Autonomous driving and Advance Drive Assistance System. We propose an approach for vision-based road detection which exploits road shape prior, color consistence representation and discrimination analysis. Firstly, road shape prior probability map is constructed from available road images which have been annotated by matching and classifying of road shape feature. Secondly, based on the road shape prior probability map, for one thing, road samples are trained to obtain the best combining coefficient of multiple color channels, so as to obtain consistent representation probability map based on minimal variance criteria, for another, road and off-road samples are extracted for discrimination analysis to obtain discrimination probability map. Finally, three maps are combined to obtain road detection result. Experiments on both public and our own database demonstrate the effectiveness of the proposed method under complex illumination conditions.

A Practical Eye State Recognition Based Driver Fatigue Detection Method

Driving fatigue detection is a key technique in vehicle active safety. In this paper, a practical driver fatigue detection algorithm is proposed, it employs sequential detection and temporal tracking to detect human face, which combines the superiorities of both Adaboost and Mean-Shift algorithm; A morphologic filter method is given to localize the pair of eyes in the detected face area. Then multiple image features are exploited to recognize open state or close state. Various tests demonstrated that it has a performance of high detection precision and fast processing speed. To this end, it can be effectively and efficiently used in vehicle active safety systems.