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基于协方差流形的异常驾驶行为识别方法
Alternative TitleAbnormal driving behavior detection based on covariance manifold
李此君1,2,3,4; 刘云鹏1,2,3,4
Department光电信息技术研究室
Source Publication太赫兹科学与电子信息学报
ISSN2095-4980
2018
Volume16Issue:2Pages:323-329
Contribution Rank1
Keyword异常驾驶行为识别 协方差描述子 黎曼流形 多类LogitBoost分类器
Abstract研究一种高效的异常驾驶行为正确识别分类的识别方法,对预防由于异常驾驶行为导致的交通事故具有重要意义。提出了一种新的基于协方差流形的异常驾驶行为识别方法。首先提取图像的纹理、颜色和梯度方向特征,以克服基于单一特征识别驾驶行为的不足;并利用协方差流形进行多特征融合,以消除特征冗余以及不同特征数值悬殊对图像识别的影响;最后使用多类LogitBoost分类器进行分类识别。针对相同检测目标的正确识别率可达98%以上,对不同检测目标的正确识别率可达70%以上。实验结果表明该方法有效提高了驾驶行为识别的效果。
Other AbstractAbnormal driving behavior recognition is to find a method to recognize abnormal driving behaviors correctly by analyzing the driver’s activities using image processing and pattern recognition technology. This method is composed of a structure of covariance matrices of image features, which is able to extract information from data. The proposed classification framework consists in a new multi-class boosting method, working on the manifold Sym+d of symmetric positive definite d *d (covariance) matrices. The correct recognition rate for the same target can reach 98%, and above 70% for different targets. The result shows that this method effectively improves the accuracy of abnormal driving behavior recognition.
Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21883
Collection光电信息技术研究室
Corresponding Author李此君
Affiliation1.中国科学院沈阳自动化研究所;
2.中国科学院大学;
3.中国科学院光电信息处理重点实验室;
4.辽宁省图像理解与视觉计算重点实验室
Recommended Citation
GB/T 7714
李此君,刘云鹏. 基于协方差流形的异常驾驶行为识别方法[J]. 太赫兹科学与电子信息学报,2018,16(2):323-329.
APA 李此君,&刘云鹏.(2018).基于协方差流形的异常驾驶行为识别方法.太赫兹科学与电子信息学报,16(2),323-329.
MLA 李此君,et al."基于协方差流形的异常驾驶行为识别方法".太赫兹科学与电子信息学报 16.2(2018):323-329.
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