Date of Award:

12-2008

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Robert T Pack

Committee

Robert T. Pack

Committee

David G. Tarboton

Committee

Kevin C. Womack

Committee

Christopher M. U. Neale

Abstract

This thesis outlines the development of a mathematical model which can be used to perform 3D reconstruction of a target object from surveillance images. 3D reconstruction is a common procedure in photogrammetry, but performing 3D reconstruction from surveillance images can be more difficult than typical photogrammetry applications.

Surveillance images are generally captured in an unsystematic manner because there is no control over the target that is being photographed. Surveillance images can have a wide variety of fields of view, are often captured with uncalibrated cameras, and typically the targets are objects for which there is no other a priori information. For these reasons, performing 3D reconstruction from surveillance images may not be possible using standard photogrammetric methods, especially when the angular fields of view of the images are rather narrow.

Several alternative methods and algorithms have been developed in photogrammetry to handle some of the complications mentioned above. For example, close-range photogrammetry methods are designed to deal with situations where images are captured from varying and random aspects. However, the majority of these models were not designed to accommodate images with a narrow angular field of view. In satellite imagery, sensor models have been created which are well-suited for narrow angular fields of view, but these models generally assume images that were captured in a systematic manner with available ground control information. Hence, existing models and methods may not be adequate to perform 3D reconstruction from surveillance images in all situations.

The model developed herein is a robust model based on principles from close range photogrammetry, satellite imagery, and computer vision. Previous work has been relied upon, and routines from several areas have been tied together to form a comprehensive algorithm that is capable of accurate 3D reconstruction in a wide variety of circumstances. The flexibility and precision of the model are demonstrated using several sets of actual surveillance images and a series of synthetic images.

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