Graduation Year

2012

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Computer Science and Engineering

Major Professor

Dmitry B. Goldgof, Ph.D.

Committee Member

Lawrence O. Hall, Ph.D.

Committee Member

Rangachar Kasturi, Ph.D.

Committee Member

Lawrence Langebrake, M.S.

Keywords

Non-Stationary Camera, Ship Detection, Horizon Detection, Vanishing Line, Stochastic Texture, SIFT Keypoints

Abstract

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.

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