Utilization of robust video processing techniques to aid efficient object detection and tracking

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.