Date of Award
1-1-2011
Document Type
Masters Thesis
Degree Name
M.S.
Organizational Unit
Daniel Felix Ritchie School of Engineering and Computer Science
First Advisor
Mohammad H. Mahoor, Ph.D.
Second Advisor
Richard M. Voyles, Ph.D.
Third Advisor
Matthew Rutherford
Keywords
Computer vision applications, Fuzzy set theory, Kalman filtering, Motion estimation, Scale-invariant feature transform, Video stabilization
Abstract
Video stabilization removes unwanted motion from video sequences, often caused by vibrations or other instabilities. This improves video viewability and can aid in detection and tracking in computer vision algorithms. We have developed a digital video stabilization process using scale-invariant feature transform (SIFT) features for tracking motion between frames. These features provide information about location and orientation in each frame. The orientation information is generally not available with other features, so we employ this knowledge directly in motion estimation. We use a fuzzy clustering scheme to separate the SIFT features representing camera motion from those representing the motion of moving objects in the scene. Each frame's translation and rotation is accumulated over time, and a Kalman filter is applied to estimate the desired motion. We provide experimental results from several video sequences using peak signal-to-noise ratio (PSNR) and qualitative analysis to demonstrate the results of each design decision we made in the development of this video stabilization method.
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Kevin Veon
Provenance
Received from ProQuest
File Format
application/pdf
Language
en
File Size
147 p.
Recommended Citation
Veon, Kevin, "Video Stabilization Using SIFT Features, Fuzzy Clustering, and Kalman Filtering" (2011). Electronic Theses and Dissertations. 675.
https://digitalcommons.du.edu/etd/675
Video Examples
Copyright date
2011
Discipline
Computer engineering, Computer science