Motion smoothing strategies for 2D video stabilization

Abstract

Video stabilization aims at removing the undesirable effects of camera motion by estimating its shake and applying a smoothing compensation. This paper proposes a unified mathematical analysis and classification of existing smoothing strategies. We assume that the apparent velocity induced by the camera is estimated as a set of global parametric models, typically those of a homography. We classify the existing smoothing strategies into compositional and additive methods and discuss their technical issues, particularly the definition of the boundary conditions. Our discussion of the various alternatives leads to clear-cut conclusions. It rules out the global compositional methods in favor of local linear methods and finds the adequate boundary conditions. We also show that the best smoothing strategy yields a scale-space analysis of the camera ego-motion parameters. Analyzing this scale-space on examples, we show how it is highly characteristic of the camera path, permitting us to compute ego-motion frequencies and to detect periodic ego-motions like walking or running.