Abstract
The problem of unmanned supervision of maritime areas has attracted the interest of researchers for the last few years, mainly thanks to the advances in vessel monitoring that the Automatic Identification System (AIS) has brought. Several frameworks and algorithms have been proposed for the management of vessel trajectory data, which focus on data compression, data clustering, classification and visualization, offering a wide variety of solutions from vessel monitoring to automatic detection of complex events. This work builds on our previous work in the topic of automatic detection of Search and Rescue (SAR) missions, by developing and evaluating a methodology for classifying the trajectories of vessels that possibly participate in such missions. The proposed solution takes advantage of a synthetic trajectory generator and a classifier that combines a genetic algorithm (GENDIS) for the extraction of informative shapelets from training data and a transformation to the shapelets’ feature space. Using the generator and several SAR patterns that are formally described in naval operations bibliography, it generates a synthetic dataset that is used to train the classifier. Evaluation on both synthetic and real data has very promising results and helped us to identify vessel SAR maneuvers without putting any effort into manual annotation.
Highlights
The compulsory use of Automatic Identification System (AIS) for many vessel types, which has been enforced by naval regulations, has opened new opportunities for maritime surveillance (according to the Regulation 19 of SOLAS Chapter V, since 2002 all vessels over 300 Gross Tonnage and all passenger vessels must carry an AIS transmitter)
The unsupervised approaches that already exist in the literature, show that using some simplistic rules [2] that seek for repetitive changes in speed and orientation or using a spatial clustering of important trajectory points [13] is enough for distinguishing between a normal movement and a potential Search and Rescue (SAR) maneuver
Since there is no previous work that focuses on this level of detail and there is no manually annotated dataset that contains information about the type of maneuvers performed be the vessels, we evaluated our approach mainly on synthetic data and we demonstrate its use of a real dataset that comprises trajectories of vessels that have been engaged in SAR missions
Summary
The compulsory use of Automatic Identification System (AIS) for many vessel types, which has been enforced by naval regulations, has opened new opportunities for maritime surveillance (according to the Regulation 19 of SOLAS Chapter V (http://solasv.mcga.gov.uk/regulations/ regulation19.htm), since 2002 all vessels over 300 Gross Tonnage and all passenger vessels must carry an AIS transmitter). The collection, processing and visualisation of AIS data from multiple vessels has been the main business for many companies, such as MarineTraffic (https://www.marinetraffic.com/), VesselTracker (https://www.vesseltracker.com/), MariWeb (https://imisglobal.com/mariweb/) and others that have develop and maintain their own world-wide networks of shore-based stations and regional data aggregators. Such platforms provide real-time information on ship’s position and information on arrivals and departures from ports, using a network of shore stations that collect and pre-process AIS signals (for missing values, errors and redundancies) and forward the result to a centralized processing server for further cleaning, post-processing and visualization. In a parallel line of research, several works on trajectory data analytics have contributed platforms for the visualisation of vessel trajectories [5,6], efficient handling of large volumes of such spatio-temporal data streams [7,8] and methodologies for the abstraction of AIS data collected for a period and a region [9]
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