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The automatic identification system (AIS) and video cameras have been widelyexploited for vessel traffic surveillance in inland waterways. The AIS datacould provide the vessel identity and dynamic information on vessel positionand movements. In contrast, the video data could describe the visualappearances of moving vessels, but without knowing the information on identity,position and movements, etc. To further improve vessel traffic surveillance, itbecomes necessary to fuse the AIS and video data to simultaneously capture thevisual features, identity and dynamic information for the vessels of interest.However, traditional data fusion methods easily suffer from several potentiallimitations, e.g., asynchronous messages, missing data, random outliers, etc.In this work, we first extract the AIS- and video-based vessel trajectories,and then propose a deep learning-enabled asynchronous trajectory matchingmethod (named DeepSORVF) to fuse the AIS-based vessel information with thecorresponding visual targets. In addition, by combining the AIS- andvideo-based movement features, we also present a prior knowledge-drivenanti-occlusion method to yield accurate and robust vessel tracking resultsunder occlusion conditions. To validate the efficacy of our DeepSORVF, we havealso constructed a new benchmark dataset (termed FVessel) for vessel detection,tracking, and data fusion. It consists of many videos and the corresponding AISdata collected in various weather conditions and locations. The experimentalresults have demonstrated that our method is capable of guaranteeinghigh-reliable data fusion and anti-occlusion vessel tracking.

Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways