University of Houston

Change detection from video recordings is critical in many applications related to surveillance, medical diagnosis, remote sensing, condition assessment, and motion segmentation, and advanced driver assistance systems. The main goal of the change detection using videos is to identify the set of pixels that are significantly different between spatially aligned images that are temporally separated. This is an extremely challenging problem because of a variety of factors, including changes in the illumination over time, appearance or disappearance of objects in the scene, and the need for temporal synchronization of the videos. Moreover, when a mobile video acquisition platform is used, a change in scale of the observed scene along with rotation and translation changes between image pairs is introduced. Thereby, the imaging geometry cannot be modeled by ordinary transform constraints because of the varying field-of-view. Over the years, many standard image processing techniques have been leveraged to realize a solution to the problem of change detection. Each potential approach attempts to exploit properties of the image, the application domain, or a combination. It would be beneficial to have a framework that analyzes the changes between videos in an automated manner. In this dissertation, I explore more complex imaging models for solving the change detection task and propose a complete framework that accomplishes spatiotemporal registration and change detection.