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。

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-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-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.