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Increasing maritime situation awareness via trajectory detection, enrichment and recognition of events

Vouros, G., Vlachou, A., Santipantakis, G., Doulkeridis, C., Pelekis, N., Georgiou, H., Theodoridis, Y., Patroumpas, K., Alevizos, E., Artikis, A., Fuchs, G., Mock, M., Andrienko, G. ORCID: 0000-0002-8574-6295, Andrienko, N. ORCID: 0000-0003-3313-1560, Claramunt, C., Ray, C., Camossi, E. and Jousselme, A-L. (2018). Increasing maritime situation awareness via trajectory detection, enrichment and recognition of events. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10819, pp. 130-140. doi: 10.1007/978-3-319-90053-7_13

Abstract

The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories’ detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results.

Publication Type: Article
Additional Information: The final authenticated version is available online at https://doi.org/10.1007/978-3-319-90053-7_13
Publisher Keywords: Big Spatio-temporal Data, Moving Objects, Trajectory Detection, Data Integration, Events Recognition/Forecasting
Subjects: H Social Sciences > HE Transportation and Communications
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
V Naval Science > V Naval Science (General)
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: http://openaccess.city.ac.uk/id/eprint/20034
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