We report on the design and evaluation of the initial results of operation of a prototype of an advanced system
for maritime security. The system is autonomous and is designed to remain in the ocean for extended periods
up to two months. It is based on the Bottom Stationing Ocean Profiler (BSOP), an un-tethered, autonomous
platform that stations itself on the sea floor and ascends to the surface at specific time intervals or, potentially,
when triggered by certain events such as recognizable acoustic signals, collected and analyzed on board. The
surface operations of the system include optical data acquisition, image data analysis, communication with
the ground station, and retrieval based functionality. The system is designed to take video and imagery of the
surrounding ocean surface and analyze it for the presence of ships, thus, potentially enabling automatic detection
and tracking of marine vehicles as they transit in the vicinity of the platform. The system transmits the data to
the ground control via bi-directional RF satellite link and can have its mission parameters reprogrammed during
the deployment. The described unit is low cost, easy to deploy and recover, and does not reveal itself to the
potential targets. The paper describes the system hardware, architecture, algorithms for visual ship detection
and tracking.
This paper presents a new technique for automatic detection of marine vehicles in open sea from a buoy camera system
using computer vision approach. Users of such system include border guards, military, port safety and flow
management, sanctuary protection personnel. The system is intended to work autonomously, taking images of
the surrounding ocean surface and analyzing them on the subject of presence of marine vehicles. The goal of the system
is to detect an approximate window around the ship and prepare the small image for transmission and human evaluation.
The proposed computer vision-based algorithm combines horizon detection method with edge detection and post-processing.
The dataset of 100 images is used to evaluate the performance of proposed technique. We discuss promising
results of ship detection and suggest necessary improvements for achieving better performance.
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