Online Event Recognition from Moving Vessel Trajectories
This system addresses the need for effective, real-time monitoring of maritime activity to notify authorities about emergencies like collisions or suspicious moves, though it appears incremental in applying existing methods to this domain.
The authors tackled the problem of real-time maritime surveillance by developing a system for online event recognition from moving vessel trajectories, which successfully validated performance, efficiency, and robustness against scalable real-world and synthetic datasets.
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.