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https://hdl.handle.net/123456789/1417| Τύπος: | Άρθρο σε επιστημονικό περιοδικό |
| Τίτλος: | A deep learning streaming methodology for trajectory classification |
| Συγγραφέας: | [EL] Κοντόπουλος, Ιωάννης[EN] Kontopoulos, Ioannis [EL] Μακρής, Αντώνιος[EN] Makris, Antonios |
| Επικεφαλής ερευνητικής ομάδας: | [EL] Τσερπές, Κωνσταντίνος[EN] Tserpes, Konstantinos |
| Ημερομηνία: | 08/04/2021 |
| Περίληψη: | 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. |
| Γλώσσα: | Αγγλικά |
| Σελίδες: | 37 |
| DOI: | 10.3390/ijgi10040250 |
| EISSN: | 2220-9964 |
| Θεματική κατηγορία: | [EL] Επιστήμη ηλεκτρονικών υπολογιστών και Πληροφορική, άλλοι τομείς[EN] Computer and Information sciences, miscellaneous [EL] Πληροφοριακά συστήματα[EN] Information Systems |
| Λέξεις-κλειδιά: | trajectory classification; deep learning; Neural Networks; computer vision; distributed processing; stream processing; real-time vessel monitoring; trajectory compression; AIS |
| Κάτοχος πνευματικών δικαιωμάτων: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
| Όροι και προϋποθέσεις δικαιωμάτων: | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). |
| Διατίθεται ανοιχτά στην τοποθεσία: | https://www.researchgate.net/publication/350747556_A_Deep_Learning_Streaming_Methodology_for_Trajectory_Classification |
| Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη: | https://www.mdpi.com/2220-9964/10/4/250 |
| Ηλεκτρονική διεύθυνση περιοδικού: | https://www.mdpi.com/2220-9964/10/4/250 |
| Τίτλος πηγής δημοσίευσης: | SPRS International Journal of Geo-Information |
| Τεύχος: | 4 |
| Τόμος: | 10 |
| Σελίδες τεκμηρίου (στην πηγή): | Article no 250 |
| Σημειώσεις: | This research was co-financed by Greece and the European Union (European Social Fund (ESF)) through the Operational Programme “Human Resources Development, Education and Lifelong Learning 2014–2020” in the context of the project “A global, distributed surveillance system for early detection and analysis of anomalous vessel trajectories (GLASSEAS)” (MIS 5049026). |
| Εμφανίζεται στις συλλογές: | Ερευνητικές ομάδες |
Αρχεία σε αυτό το τεκμήριο:
| Αρχείο | Περιγραφή | Σελίδες | Μέγεθος | Μορφότυπος | Έκδοση | Άδεια | |
|---|---|---|---|---|---|---|---|
| ijgi-10-00250 (2).pdf | 2.9 MB | Adobe PDF | - |  | Δείτε/ανοίξτε |