Dutta, Rahul, authorDraper, Bruce A., advisorBeveridge, Ross, advisorPeterson, Chris, committee member2015-08-272015-08-272015http://hdl.handle.net/10217/166958Handheld videos often include unintentional motion (jitter) and intentional motion (pan and/or zoom). Human viewers prefer to see jitter removed, creating a smoothly moving camera. For video analysis, in contrast, aligning to a fixed stable background is sometimes preferable. This paper presents an algorithm that removes both forms of motion using a novel and efficient way of tracking background points while ignoring moving foreground points. The approach is related to image mosaicing, but the result is a video rather than an enlarged still image. It is also related to multiple object tracking approaches, but simpler since moving objects need not be explicitly tracked. The algorithm presented takes as input a video and returns one or several stabilized videos. Videos are broken into parts when the algorithm detects background change and it becomes necessary to fix upon a new background. We present two techniques in this thesis. One technique stabilizes the video with respect to the first available frame. Another technique stabilizes the videos with respect to a best frame. Our approach assumes the person holding the camera is standing in one place and that objects in motion do not dominate the image. Our algorithm performs better than previously published approaches when compared on 1,401 handheld videos from the recently released Point-and-Shoot Face Recognition Challenge (PASC).born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.foreground segmentationvideo stabilizationtrackingcomputer visionText