Finding Abnormal Vessel Trajectories Using Feature Learning

Global Positioning System technology has been widely used in vehicle tracking and road planning applications. An enormous amount of data concerning the trajectories of vehicles has been collected and stored for tracking purposes. A trajectory contains not only the footprints of a moving object but also additional information, such as speed and stopping points. Therefore, the large-scale trajectory data sets provide rich information and are currently attracting considerable attention; there have been many successful studies of event detection based on trajectory data. However, most of these studies have focused only on vehicles traveling in a road network and have note considered maritime trajectories. A maritime trajectory also contains auxiliary data (e.g., speed and rotation) in addition to the movements of a ship. However, ships are not bound to road networks, and consequently, it is difficult to apply traditional mining algorithms based on road networks. In addition, even if the amount of maritime trajectory data is very large, these data are also spatially sparse, which will significantly reduce the effectiveness of most existing mining algorithms. In this paper, we propose a new method of abnormal trajectory detection to address this problem. This method can detect abnormal vessel trajectories from Automatic Identification System (AIS), records for vessels via our feature learning algorithm. To reduce the search space, we invoke reference points as well as the Piecewise Linear Segmentation (PLS), algorithm to compress the trajectories without losing important information. A time-aware and spatially correlated collaborative algorithm is proposed to increase the density of the trajectories to improve the accuracy of the detection algorithm, which is based on Dynamic Time Warping (DTW). Finally, we report experiments conducted on a real-world data set, which demonstrate that the proposed detection method can detect anomalous trajectories effectively.