Abstract
Video magnification reveals subtle changes invisible to
the naked eye, but such tiny yet meaningful changes are
often hidden under large motions: small deformation of
the muscles in doing sports, or tiny vibrations of strings in
ukulele playing. For magnifying subtle changes under large
motions, video acceleration magnification method has recently been proposed. This method magnifies subtle acceleration changes and ignores slow large motions. However,
quick large motions severely distort this method. In this paper, we present a novel use of jerk to make the acceleration
method robust to quick large motions. Jerk has been used
to assess smoothness of time series data in the neuroscience
and mechanical engineering fields. On the basis of our observation that subtle changes are smoother than quick large
motions at temporal scale, we used jerk-based smoothness
to design a jerk-aware filter that passes subtle changes only
under quick large motions. By applying our filter to the acceleration method, we obtain impressive magnification results better than those obtained with state-of-the-art.