Repository logo
 

Video alignment to a common reference

Simple item page
dc.contributor.authorDutta, Rahul, author
dc.contributor.authorDraper, Bruce A., advisor
dc.contributor.authorBeveridge, Ross, advisor
dc.contributor.authorPeterson, Chris, committee member
dc.date.accessioned2015-08-27T03:57:11Z
dc.date.available2015-08-27T03:57:11Z
dc.date.issued2015
dc.description.abstractHandheld 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).
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierDutta_colostate_0053N_12923.pdf
dc.identifier.urihttp://hdl.handle.net/10217/166958
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright 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.
dc.subjectforeground segmentation
dc.subjectvideo stabilization
dc.subjecttracking
dc.subjectcomputer vision
dc.titleVideo alignment to a common reference
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Dutta_colostate_0053N_12923.pdf
Size:
31.56 MB
Format:
Adobe Portable Document Format
Download

Collections

2000-2019
Theses and Dissertations