Due to the vast amount of available tracking sensors in recent years, high-frequency and high-volume streams of data are generated every day. 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. Consequently, automated methodologies able to extract meaningful information from high-frequency, large volumes of vessel tracking data need to be developed. 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. 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. 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. 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.

中文翻译：

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轨迹分类的深度学习流方法

由于近年来有大量可用的跟踪传感器，因此每天都会生成大量的高频数据流。海洋领域没有什么不同，因为所有大型船只都必须配备定期跟踪其位置的船只跟踪系统。因此，需要开发能够从高频，大量船只跟踪数据中提取有意义的信息的自动化方法。实时从此类数据中自动识别船舶流动性至关重要，因为它可以在适当的时间揭示异常或非法的船舶活动。因此，在这项工作中，我们提出了一种新颖的方法，该方法可以将流船轨迹模式转换为图像，并采用深度学习算法以接近实时的方式准确地对船活动进行分类，以应对大数据量和速度的挑战。从陆地船只跟踪接收器收集的两个真实世界数据集被用于评估分类和流执行性能方面的拟议方法。实验结果表明，血管活动分类性能可以达到超过$96％$ 同时在流执行性能上实现亚秒级的延迟。