Research On Key Issues In Vehicle Safety Driving Assistant Techniques Based On Computer Vision

Vehicle driving safety is a persistent issue in transportation industry.In recent years,along with the growth of the amount of cars,the traffic accidents arise more and more frequently,bringing huge damages to the society.Under this circumstance,Vehicle safety driving assistant technique has drawn great attentions.It is a frontier research topic,and also a effective measurement for preventing the happening and damage of traffic accident.Although a few of the vehicle driving-assistant techniques have been used in practical systems,most of the techniques remain immature.This dissertation researches the vehicle driving assistant techniques,mainly on the following two topics:driver fatigue detection,and outside-car pedestrian detection.The purpose of eye and mouth detection is to detect the closed eye and yawning mouth which are two major features of fatigue.According to the different appearance of different eye and mouth statuses,we propose a statical learning based method.We treat the status detection problem as a classification problem which is to recognize the abnormal status(closed eye or yawning) from eye and mouth images.We propose to use LBP Histogram Bin Distance(LBPH-BIN-DIST) as an effective feature extraction method.Based on this feature,we construct a cascade AdaBoost classifier to detect the abnormal status.The proposed method is highly accurate and fast.The purpose of head pose estimation is to detect the long-time lower or higher head poses,or frequent nodding.These affairs are also an important clue of fatigue.The research consists of two steps.The first step is to accurately locate several facial feature points,such as eye and mouth corners.Then,the second step uses these points for pose estimation.The detection of feature points is based on the grayscale distribution of the neighborhood around these points.We utilize the distribution to extract features, and use logistic regression to fuse several features,and give the final estimation of the feature points.Once we have achieved the location of these feature points,we can use POSIT algorithm for pose estimation.To fuse the available fatigue clues,such as eye and mouth status the head pose, also to integrate other external factors that will lead to driver fatigue,such as sleeping quality,time and weather,we use Bayes network to fuse all of these information and to get a final estimation of the probability of fatigue.The fatigue estimation model built by Bayes network considers the correlation and the conditional probabilities of different factors,thus can give more reasonable and overall judgement of fatigue.Besides the fatigue detection,the outside-car pedestrian detection is another important issue in driving-assistant research.The aim is to monitor the pedestrian activity, and prevent the potential accidents against pedestrians.Regarding this problem,we use a method based on the model of human body configuration.We first use rectangular to simulate the torso and limbs,and construct a walking human body model.Then,we use chamfer matching algorithm to find the potential human body regions in the images. The algorithm will give several candidates for human body.To identify the real human body,we use hidden Markov model(HMM) decoding algorithm.Some priors about human body are also added into the model as global constraints.The experiment shows that our method can effective detect pedestrian with low false-alarm rate.