Infrared and Laser Engineering, Volume. 46, Issue 5, 528001(2017)
Trajectory outlier detection based on DBSCAN clustering algorithm
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.
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Zhou Peipei, Ding Qinghai, Luo Haibo, Hou Xinglin. Trajectory outlier detection based on DBSCAN clustering algorithm[J]. Infrared and Laser Engineering, 2017, 46(5): 528001
Category: 景象信息处理
Received: Sep. 11, 2016
Accepted: Oct. 20, 2016
Published Online: Jul. 10, 2017
The Author Email: Peipei Zhou (zhoupeipei@sia.cn)