Anomalous behavior detection method based on multidimensional trajectory characteristics

被引：0
|
作者：
Pan X. [1 ]
Wang H. [1 ]
He Y. [1 ]
Xiong W. [1 ]
Zhou W. [1 ]
机构：
[1] Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2017年 / 38卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Anomalous behavior; Hausdorff distance; Local outlier factor; Multidimensional characteristics; Trajectory;
D O I：
10.7527/S1000-6893.2016.0217
中图分类号：
学科分类号：
摘要：
In the information fusion domain, anomalous behaviors could be mined based on multidimensional trajectory characteristics by using the anomalous detection technique in data mining. Previous trajectory anomaly detection algorithms mainly detect the position anomalies, without making full use of the attribute, category, position, velocity, and course characteristics. In order to overcome this limitation, we define the multi-factor Hausdorff distance, construct the multidimensional local outlier factor, and propose a method for detecting anomalous behaviors based on multidimensional trajectory characteristics. The method can mine anomalous behaviors based on detecting multidimensional trajectories. We conducted experiments on simulated military scenario and real civilian scenario, the proposed method can effectively detect the anomalous behavior of the target. © 2017, Press of Chinese Journal of Aeronautics. All right reserved.
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