Qualitative Assessment of Video Stabilization and Mosaicking Systems

Abstract

Image stabilization is a key preprocessing step in dynamic image analysis, which deals with the removal of unwanted motion in a video sequence. It is principally understood as the warping of video sequences resulting in a total or partial removal of image motion. Stabilization is invaluable for motion analysis, structure from motion, independent motion detection, geo-registration and mosaicking, autonomous vehicle navigation, model-based compression, and many others. Given the usefulness of image stabilization for many applications, a variety of algorithms have been proposed to perform this task, and many real-time systems have been built to stabilize the real-time videos and provide motion data for tracking and geo-registrations. However, even though there are on-line libraries that provide test videos, there has been no established methods or industrial standards based on which the performance of a stabilization algorithm or system can be measured. This paper aims to address this gap and suggests an evaluation methodology which would provide us the ability to qualitatively measure the performance of a given stabilized system. We propose a performance measurement system and define the performance metrics in this paper. We then apply the assessment to two typical stabilization systems. The discussed methods can be used to benchmark video stabilization systems.