The use of radar-based systems for vessel monitoring is not suitable in populated areas, due to the high electromagnetic emissions. In this paper, a camera based vessel recognition system for application in the context of Vessel Traffic Services (VTS) and Homeland Protection (HP) is proposed. Our approach is designed to extend the functionality of traditional VTS systems by permitting the classification of both cooperative and non-cooperative targets, using camera images only. This allows enhancing the surveillance function in populated areas, where public opinion is strongly concerned about electromagnetic emissions and therefore antennas are suspiciously observed and radars are not allowed. Experiments have been carried out on a publicly available data set of images coming from the ARGOS boat traffic monitoring system in the City of Venice (Italy). The obtained classification accuracy of 89.6% (with 11 different classes of boats) demonstrates the effectiveness of the proposed approach.

The constant increase in marine traffic and the simultaneous growth of the demand for exploiting marine areas (e.g., installing offshore wind power plants) require an adequate planning strategy for managing high traffic volumes. Maritime Spatial Planning (MSP) is the process of public development of an allocation plan for distributing, both spatially and temporally, human activities in marine areas. The adoption of e-Navigation is a possible solution for improving safety and security at sea by integrating maritime information on board and ashore. Automatic Identification System (AIS) data represents a fundamental source of information, since the analysis of AIS data can highlight the presence of congested areas as well as of illegal actions, such as smuggling, pollution, and unauthorized phishing in protected areas. Indeed, those activities are often characterized by abnormal manoeuvres that can be recognized by analyzing the routes of the vessels. However, the huge dimension of the AIS data to process requires the adoption of careful strategies for the data visualization. In this paper, we present a complete pipeline for visualizing ship routes from raw AIS data, which is a fundamental pre-requisite for carrying out a significant AIS-based route analysis, and describe a real case study, where 90 million AIS records, corresponding to one month of world-wide observations, are visualized using only open-source software.