Venilia, On-line Learning and Prediction of Vessel Destination

Bachar, Moti; Elimelech, Gal; Gat, Itai; Sobol, Gil; Rivetti, Nicolò; Gal, Avigdor

doi:10.1145/3210284.3220505

Cited by 2 publications

(2 citation statements)

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“…On the other hand, trajectory clustering approaches are often employed to form groups of AIS positions with similar spatiotemporal behaviors, uncovering behaviors that are harder to predefine. Although there is an abundance of studies in the literature regarding offline trajectory classification and clustering [1][2][3][4][5], fewer works have focused on steam processing of events in the maritime domain [6][7][8][9][10]. Event processing methodologies are faced with significant challenges when employed on streaming data where the requirements for such applications demand low memory consumption and decreased latencies.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an image classification approach for trajectory classification yields a promising universal approach for the classification of mobility patterns; • Approximately 16,000 AIS messages are generated each second from 200,000 vessels worldwide, resulting in 46GB of data per day. In the maritime domain, only in recent years have researchers started tackling the problem of real-time stream processing with the use of AIS messages [6][7][8][9][10]. To the best of our knowledge, this is the first time in the maritime domain literature that computer vision techniques have been used in real time to classify trajectories.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Learning Streaming Methodology for Trajectory Classification

Kontopoulos

Makris

Tserpes

2021

IJGI

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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.

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