Effective Ship Trajectory Imputation with Multiple Coastal Cameras

The ship trajectories collected by the Automatic Identification System (AIS) are widely used in maritime applications. However, a significant issue with AIS data is that large AIS gaps occur. Existing trajectory imputation methods for AIS data have three main limitations: (1) the temporal aspect is ignored; (2) the methods fall short when dealing with complex ship movements; (3) the common-route assumption does not always hold. To overcome these limitations, we propose TrajImpMC, a tracking-based framework that uses polygon-based ship location estimates from multiple cameras to impute large AIS gaps. TrajImpMC combines speed constraints and Kalman filters, and can return imputed trajectories that contain both spatial and temporal information. Extensive experiments are conducted on real datasets. In terms of the quality of the imputed trajectories, TrajImpMC improves the RMSE errors by at least one order of magnitude over two existing state-of-the-art AIS imputation methods. In addition, a visual comparison shows that the imputed trajectories of TrajImpMC align very well with the real ship trajectories during AIS gaps. The code for this paper is available at: https://github.com/songwu0001/TrajImpMC.