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      Trajectory Outlier Detection Algorithm Based on Bi-LSTM Model

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          Abstract

          The rapid advances in positioning technology have created huge spatio-temporal trajectory data, and there are always obvious aberrant outliers in trajectory data. Detecting outliers in the trajectory is critical to improving data quality and the accuracy of subsequent trajectory data mining tasks. In this paper, we propose a trajectory outlier detection algorithm based on a Bidirectional Long Short-Term Memory (Bi-LSTM) model. First, a six-dimensional motion feature vector is extracted for each trajectory point, and then we construct a Bi-LSTM model. The model input is the trajectory data feature vector of a certain sequence length, and its output is the class type of the current track point. In addition, a combination method of undersampling and oversampling is applied to mitigate the effect of data distribution imbalance on detection performance. The Bi-LSTM model can automatically learn the difference between the normal points and adjacent abnormal points in the motion characteristics by combining the LSTM unit and the bidirectional network. Experimental results based on a real ship trajectory annotation data show that the detection performance of our proposed algorithm significantly exceeds those of the constant velocity threshold algorithm, non-sequential classical machine learning classification algorithms, and convolutional neural network model. Especially, the recall value of the proposed algorithm reaches 0.902, which verifies its effectiveness.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 February 2019
          : 8
          : 1
          : 36-43
          Affiliations
          [1 ] ①(University of Chinese Academy of Sciences, Beijing 100049, China)
          [2 ] ②(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
          Article
          56ea42d904cd4aa395ae72a66b97bdee
          10.12000/JR18039

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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