Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains ix, 264 p.; also includes grafics (some col.). Includes abstract and vita. Advisor: Lesley K. Ferris, Dept. of Theatre. Includes bibliographical references (p. 246-264).

Thesis (M.A.)--Kutztown University of Pennsylvania, 2003. / Source: Masters Abstracts International, Volume: 45-06, page: 2724. Typescript. Abstract precedes thesis as preliminary leaves iii-iv. Includes bibliographical references (leaves 112-113).

Thesis (M.A.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains iv, 41 p. Includes abstract. Includes bibliographical references (p. 28-34).

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains vi, 52 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 50-52).

Thesis (Ed. D.)--Columbia University, 1963. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 391-413).

Thesis (Ph. D.)--University of Oregon, 2000. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 119-134). Also available for download via the World Wide Web; free to University of Oregon users.

Thesis (Ph. D.)--University of Texas at Austin, 2002. / Vita. Includes bibliographical references. Available also from UMI Company.

Includes bibliographical references (p. 73-75).

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

Visual surveillance in dynamic scenes, especially for human activities, is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism and crime to ensure public safety. The motivation of this thesis is to design an efficient human visual tracking system for video surveillance deployed in complex environments. In video surveillance, detection of moving objects is the first step to analyze the video streams. And motion segmentation is one of popular approaches to do it. In this thesis, we propose a motion segmentation method to overcome the problem of motion blurring. The task of human tracking is key to the effective use of more advanced technologies, like activity recognition and behavior understanding. However, human tracking routines often fail either due to human's arbitrary movements or occlusions by other objects. To overcome human's arbitrary movement, we propose a new Silhouette Chain Shift model for human detection and tracking. To track human under occlusions, firstly each frame is represented by a scene energy which consists of all the moving objects. Then the process of tracking is converted to a process of minimizing the proposed scene energy. Findings from the thesis contribute to improve the performance of human visual tracking system and therefore improve security in areas under surveillance. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy