Algorithms for Visual Maritime Surveillance with Rapidly Moving Camera

Visual surveillance in the maritime domain has been explored for more than a decade. Although it has produced a number of working systems and resulted in a mature technology, surveillance has been restricted to the port facilities or areas close to the coastline assuming a fixed-camera scenario. This dissertation presents several contributions in the domain of maritime surveillance. First, a novel algorithm for open-sea visual maritime surveillance is introduced. We explore a challenging situation with a camera mounted on a buoy or other floating platform. The developed algorithm detects, localizes, and tracks ships in the field of view of the camera. Specifically, our method is uniquely designed to handle a rapidly moving camera. Its performance is robust in the presence of a random relatively-large camera motion. In the context of ship detection, a new horizon detection scheme for a complex maritime domain is also developed. Second, the performance of the ship detection algorithm is evaluated on a dataset of 55,000 images. Accuracy of detection of up to 88% of ships is achieved. Lastly, we consider the topic of detection of the vanishing line of the ocean surface plane as a way to estimate the horizon in difficult situations. This allows extension of the ship-detection algorithm to beyond open-sea scenarios

Detection of Marine Vehicles in Images and Video of Open Sea

This work presents a new technique for automatic detection of marine vehicles in images and video of open sea. Users of such system include border guards, military, port safety, flow management, and sanctuary protection personnel. The source of images and video is a digital camera or a camcorder which is placed on a buoy or stationary mounted in a harbor facility. The system is intended to work autonomously, taking images of the surrounding ocean surface and analyzing them for the presence of marine vehicles. The goal of the system is to detect an approximate window around the ship. The proposed computer vision-based algorithm combines a horizon detection method with edge detection and postprocessing. Several datasets of still images are used to evaluate the performance of the proposed technique. For video sequences the original algorithm is further enhanced with a tracking algorithm that uses Kalman filter. A separate dataset of 30 video sequences 10 seconds each is used to test its performance. Promising results of the detection of ships are discussed and necessary improvements for achieving better performance are suggested

Detection and Tracking of Ships in Open Sea with Rapidly Moving Buoy-Mounted Camera System

Visual surveillance in the maritime domain has been explored for more than a decade. Although it has produced a number of working systems and resulted in a mature technology, surveillance has been restricted to the port facilities or areas close to the coast line assuming a fixed-camera scenario. This paper presents a novel algorithm for open-sea visual maritime surveillance. We explore a challenging situation with a forward-looking camera mounted on a buoy or other floating platform. The proposed algorithm detects, localizes, and tracks ships in the field of view of the camera. Specifically, developed algorithm is uniquely designed to handle rapidly moving camera. Its performance is robust in the presence of a random relatively-large camera motion. In the context of ship detection we developed a new horizon detection scheme for a complex maritime domain. The performance of our algorithm and its comprising elements is evaluated. Ship detection precision of 88% is achieved on a large dataset collected from a prototype system