![]() ![]() ![]() Several distinct use cases that demonstrate the need for increased MSA include the detection of anomalous vessel behavior due to the damage of the ship, Search And Rescue operations (SAR) and collision detections, and piracy and illegal fishing activities. Experimental results demonstrated that the vessel activity classification performance can reach an accuracy of over 96 % while achieving sub-second latencies in streaming execution performance. Two real-world data sets collected from terrestrial, vessel-tracking receivers were used to evaluate the proposed methodology in terms of both classification and streaming execution performance. Therefore, in this work, we present a novel approach that transforms streaming vessel trajectory patterns into images and employs deep learning algorithms to accurately classify vessel activities in near real time tackling the Big Data challenges of volume and velocity. The automatic identification of vessel mobility patterns from such data in real time is of utmost importance since it can reveal abnormal or illegal vessel activities in due time. Consequently, automated methodologies able to extract meaningful information from high-frequency, large volumes of vessel tracking data need to be developed. The maritime domain is no different as all larger vessels are obliged to be equipped with a vessel tracking system that transmits their location periodically. Due to the vast amount of available tracking sensors in recent years, high-frequency and high-volume streams of data are generated every day.
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