The primary source of information perused by Homeland Security is the images captured by surveillance cameras and Unmanned Aerial Vehicles (UAVs). In this research, data acquired by Unmanned Aerial Vehicles (UAVs) is primarily used to detect and track moving objects which pose a major security threat along the United States southern border. Factors such as camera motion, poor illumination and noise make detection and tracking of moving objects in surveillance videos a formidable task. The main objective of this research is to provide a less ambiguous image data for object detection and tracking by means of noise reduction, image enhancement, video stabilization, and illumination restoration. The improved data is later utilized to detect and track moving objects in surveillance videos. An optimization based image enhancement scheme was successfully implemented to increase edge information to facilitate object detection. Noise present in the raw video captured by the UAV was efficiently removed using search and match methodology. Undesired motion induced in the video frames was eliminated using block matching technique. Simulation results shows the efficiency of these image processing algorithms in processing noisy, un-stabilized raw video sequences which were utilized to detect and track moving objects in the video sequences.
July 27, 2016
The right to download or print any of the pages of this thesis (Material) is granted by the copyright owner only for personal or classroom use. The author retains all proprietary rights, including copyright ownership. Any reproduction or editing or other use of this Material by any means requires the express written permission of the copyright owner. Except as provided above, or any use beyond what is allowed by fair use (Title 17 Section 107 U.S.C.), you may not reproduce, republish, post, transmit or distribute any Material from this web site in any physical or digital form without the permission of the copyright owner of the Material. Inquiries regarding any further use of these materials should be addressed to Administration, Jernigan Library, Texas A&M University-Kingsville, 700 University Blvd. Kingsville, Texas 78363-8202, (361)593-3416.