Performance characterization of image stabilization algorithms

Abstract

This paper considers the characterization of three different image stabilization algorithms when used as a preprocessor for a computer vision application. These algorithms vary in computational complexity, accuracy, and performance. The first algorithm (developed by the Army Research Laboratory (ARL)) is capable of image alignment to an accuracy of one pixel. Algorithms 2 and 3 (developed by the University of Maryland) are capable of full subpixel stabilization with respect to translation, rotation, and scale. The evaluation tools incorporated include mean square error of the output data set and the overall performance of an automatic target acquisition (ATA) system (developed at ARL) that uses the algorithms as a front-end preprocessor. We show that for the ARL ATA application, extremely accurate subpixel stabilization is a requirement for proper operation. Based on experiments, we conclude that algorithm 3 performs significantly better than the other two algorithms.