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Alternative TitleAbnormal driving behavior detection based on covariance manifold
Thesis Advisor刘云鹏
Keyword异常驾驶行为识别 图像处理 协方差描述子 黎曼流形 多类LogitBoost分类器
Call NumberTP391.41/L31/2018
Degree Discipline控制工程
Degree Name硕士
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Other AbstractIn order to cope with the high proportion of traffic accidents caused by driver factors, auxiliary safety driving technology has become the research frontier and research hotspot in the field of intelligent transportation. Therefore, using image processing and pattern recognition technology, this paper studies a recognition method of correct identification of abnormal driving behavior by analyzing driver's active state. It is of great significance to reduce traffic accidents caused by abnormal driving behavior. In this paper, a new anomaly driving behavior recognition method based on covariance manifold and multi class LogitBoost classifier is studied. First, the image is preprocessed. By preprocessing the image and the edge detection operation, the contour and position of the steering wheel are determined, and then the region of interest which contains only the activity of the driver can be obtained, which can improve the accuracy of the follow-up method recognition and reduce the calculation amount of the follow-up method. Secondly, the texture, color and gradient direction of the driving behavior image are extracted to overcome the shortage of driving behavior based on single feature recognition, and the covariance manifold is used for multi feature fusion to eliminate the feature redundancy and reduce the possibility of the image processing and recognition due to the large difference of different features. Influence. Finally, the multi class LogitBoost classifier based on the binary classifiers is used to classify and recognize the pictures. In view of the test data sets, compared with the traditional LogitBoost multi classification method, the multi class LogitBoost classification method based on the binary classifiers has greatly improved the accuracy of multi classification. In order to verify the method of abnormal driving behavior recognition proposed in this paper, the simulation test under the MATLAB platform is carried out. The average correct recognition rate can reach 81.08% for the test data set, which effectively improves the effect of multi class abnormal driving behavior recognition.
Contribution Rank1
Document Type学位论文
Recommended Citation
GB/T 7714
李此君. 基于协方差流形的异常驾驶行为识别方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2018.
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