Rethinking two-dimensional camera motion estimation assessment for digital video stabilization: A camera motion field-based metric

Abstract

Digital video stabilization aims to remove camera motion jitters through software implementation. The first step of the classical video stabilization methodology is called camera motion estimation, which is usually performed using only RGB frames of the unstable video. Despite recent advances in camera motion estimation strategies, methods classified as two-dimensional are still not properly evaluated, even though it is well known that motion estimation is a crucial step for classical approaches to video stabilization. The main purpose of this work is to draw attention to two-dimensional camera motion estimation assessment and reinforce its importance on video stabilization progress. We proposed a new approach to perform this evaluation using camera motion fields in a pixel-by-pixel comparison and demonstrated through experimental results that our metrics are reliable for diverse scenarios comparing them to image similarity metrics. In addition, we showed and analyzed the results of our metrics for a global and a local method of camera motion estimation. We believe that our assessment and study presented in this work is an important starting point for a more rigorous analysis of this task. In addition, this can be a foundation for coming 2D camera motion estimation methods based on deep learning.