Date of Award


Document Type

Masters Thesis

Degree Name


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


Computer vision applications, Fuzzy set theory, Kalman filtering, Motion estimation, Scale-invariant feature transform, Video stabilization


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


Received from ProQuest

File Format




File Size

147 p. (3905 kB)
Video Examples


Computer engineering, Computer science