基于多维航迹特征的异常行为检测方法

doi:10.7527/S1000-6893.2016.0217

Abstract

Abstract:

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.

Key words: anomalous behavior, trajectory, multidimensional characteristics, local outlier factor, Hausdorff distance

CLC Number:

PAN Xinlong, WANG Haipeng, HE You, XIONG Wei, ZHOU Wei. Anomalous behavior detection method based on multidimensional trajectory characteristics[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017, 38(4): 320442-320442.

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Anomalous behavior detection method based on multidimensional trajectory characteristics

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