Trajectory Anomaly Detection Based on the Mean Distance Deviation

Abstract

With the development of science and technology and the explosive growth of data, there will be a lot of trajectories every day. However, how to detect the abnormal trajectory from many trajectories has become a hot issue. In order to study trajectory anomaly detection better, we analyze the Sequential conformal anomaly detection in trajectories based on hausdorff distance (SNN-CAD) method, and propose a new measurement method of trajectory distance Improved Moved Euclidean Distance (IMED) instead of Hausdorff distance, which reduces the computational complexity. In addition, we propose a removing-updating strategy to enhance the conformal prediction (CP). Then, we also put forward our Non-conformity measure (NCM), Mean Distance Deviation. It can enlarge the difference between trajectories more effectively, and detect the abnormal trajectory more accurately. Finally, based on the technical measures mentioned above and under the framework of enhanced conformal prediction theory detection, we also build our own detector called Mean Distance Deviation Detector (MDD-ECAD). Using a large number of synthetic trajectory data and real world trajectory data on two detectors, the experimental results show that MDD-ECAD is much better than SNN-CAD in both accuracy and running time.

This work has been funded by Natural Science Foundation of China under Grants No. 61471261 and No. 61771335. The author Yuejun Guo acknowledges support from Secretaria dUniversitats i Recerca del Departament dEmpresa i Coneixement de la Generalitat de Catalunya and the European Social Fund.