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http://dx.doi.org/10.11627/jksie.2021.44.4.169

An Application of Deep Clustering for Abnormal Vessel Trajectory Detection  

Park, Heon-Jei (Department of Industrial Engineering, Hannam University)
Lee, Jun Woo (GDL System)
Kyung, Ji Hoon (Department of Industrial Engineering, Hannam University)
Kim, Kyeongtaek (Department of Industrial Engineering, Hannam University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.44, no.4, 2021 , pp. 169-176 More about this Journal
Abstract
Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.
Keywords
Deep Learning; Anomaly Detection; Vessel Trajectory;
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