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题名:
基于DBSCAN聚类算法的异常轨迹检测
其他题名: Trajectory outlier detection based on DBSCAN clustering algorithm
作者: 周培培; 丁庆海; 罗海波; 侯幸林
作者部门: 光电信息技术研究室
通讯作者: 周培培
关键词: 时空异常轨迹检测 ; VMDL分割准则 ; DBSCAN聚类算法 ; 二级检测算法
刊名: 红外与激光工程
ISSN号: 1007-2276
出版日期: 2017
卷号: 46, 期号:5, 页码:238-245
收录类别: EI ; CSCD
产权排序: 1
摘要: 现有的异常轨迹检测算法往往侧重于检测轨迹的空域异常,忽略了对轨迹时域异常的检测,并且检测精确度不高,针对此类问题,提出了基于增强聚类的异常轨迹检测算法。首先,采用基于速度的最小描述长度(VMDL)准则把轨迹简化成有序线段;然后,使用改进的线段间的距离定义,基于DBSCAN算法把线段分为不同的类,以建模局部正常运动模式;最后,采用先检测空间异常性再检测时间异常性的二级检测算法,检测时空异常轨迹点。在多个测试集上的实验结果表明:该算法可以检测位置、角度、速度等三种时空异常轨迹点,相对于其他算法,明显提高了异常轨迹检测的精确度。
英文摘要: Existing traditional trajectory outlier detection algorithms always focus on spatial outliers and ignore temporal outliers, and the accuracy is relatively low. To solve these problems, a simple and effective approach based on enhanced clustering algorithm was proposed to detect spatio-temporal trajectory outliers. Firstly, each original trajectory was simplified into a set of sequential line segments with the velocity鄄based minimum description length (VMDL) partition principle. Secondly, the distance formula between line segments was improved to enhance the clustering performance. Using DBSCAN algorithm, the line segments were classified into different groups which could represent local normal behaviors. Thirdly, outliers were detected using two鄄level detection algorithm which first detected spatial outliers and then detected temporal outliers. Experimental results on multiple trajectory data sets demonstrate that the proposed algorithm could successfully detect three kinds of spatio-temporal outliers, position, angle and velocity. Compared with other methods, the precision and accuracy make great improvement.
语种: 中文
EI收录号: 20173304042260
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/20770
Appears in Collections:光电信息技术研究室_期刊论文

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Recommended Citation:
周培培,丁庆海,罗海波,等. 基于DBSCAN聚类算法的异常轨迹检测[J]. 红外与激光工程,2017,46(5):238-245.
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