Online Event Recognition from Moving Vessel Trajectories
Kostas Patroumpas,Elias Alevizos,Alexander Artikis,Marios Vodas,Nikos Pelekis,Yannis Theodoridis +5 more
TLDR
Extensive tests validate the performance, efficiency, and robustness of the system against scalable volumes of real-world and synthetically enlarged datasets, but its deployment against online feeds from vessels has also confirmed its capabilities for effective, real-time maritime surveillance.Abstract:
We present a system for online monitoring of maritime activity over streaming positions from numerous vessels sailing at sea. It employs an online tracking module for detecting important changes in the evolving trajectory of each vessel across time, and thus can incrementally retain concise, yet reliable summaries of its recent movement. In addition, thanks to its complex event recognition module, this system can also offer instant notification to marine authorities regarding emergency situations, such as risk of collisions, suspicious moves in protected zones, or package picking at open sea. Not only did our extensive tests validate the performance, efficiency, and robustness of the system against scalable volumes of real-world and synthetically enlarged datasets, but its deployment against online feeds from vessels has also confirmed its capabilities for effective, real-time maritime surveillance.read more
Citations
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Journal ArticleDOI
A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis
TL;DR: A multi-step trajectory clustering method that combines Dynamic Time Warping, a similarity measurement method, with Principal Component Analysis to decompose the obtained distance matrix and an automatic algorithm for choosing the k clusters is developed according to the similarity distance.
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Spatio-Temporal Vessel Trajectory Clustering Based on Data Mapping and Density
TL;DR: Improved density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed to cluster spatial points to acquire the optimal cluster and a fusion of the MD, MDS, and improved DBSCAN algorithms can identify the course of trajectories and attain a better clustering performance.
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Maritime anomaly detection: A review
TL;DR: This study presents a review of the state‐of‐the‐art of research carried out for the analysis of maritime data for situational awareness, and outlines possible paths of investigation and challenges for maritime anomaly detection.
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Complex event recognition in the Big Data era: a survey
TL;DR: This survey elaborates on the whole pipeline from the time CER queries are expressed in the most prominent languages, to algorithmic toolkits for scaling-out CER to clustered and geo-distributed architectural settings.
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A method for simplifying ship trajectory based on improved Douglas–Peucker algorithm
Liangbin Zhao,Guo-You Shi +1 more
TL;DR: To better compress ship trajectory data regarding compression time and efficiency, a method based on the improved Douglas–Peucker (DP) algorithm is presented and outperforms other existing trajectory compression algorithms in term of compression time.
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