Unsupervised abnormal track detection method based on GRU-VAE

Abstract

Aiming at the problem of ship behavior anomaly detection based on massive track data without behavior pattern label, an unsupervised track anomaly detection method based on GRU-VAE model is proposed. The abnormal behavior of the target is found by detecting track anomaly, which is implemented in two steps: model training stage and anomaly detection stage. In the model training stage, the timing modeling ability of GRU gated cyclic Autoencoder (VAE) model is introduced. The Gate Recurrent unit-variational Autoencoder model is trained by historical track data without abnormal information labels. According to the reconstruction loss distribution, the normal distribution method or percentile method is used to delimit the confidence interval as the reconstruction loss threshold. In anomaly detection phase, the model of real-time track data set for testing, regarding the damage threshold of the track to refactor losses above points as abnormal track points, when the track is in the sequence beyond the proportion of abnormal track point accounted threshold, it is judged to be abnormal track sequence, combined with the data anomalies target behavior information are presented to the first-line staff. The experimental results on AIS data show that the highest F1 score of the model is up to 86.36%, and the recall rate is up to 95%. The high sensitivity and low miss alarm rate of this method to abnormal track meet the reconnaissance requirements of first-line units.

Keywords

• data mining|track data|anomaly detection|unsupervised learning