  We present a method for stabilizing handheld video that simulates the camera
motions cinematographers achieve with equipment like tripods, dollies, and
Steadicams. We formulate a constrained convex optimization problem minimizing
the $\ell_1$-norm of the first three derivatives of the stabilized motion. Our
approach extends the work of Grundmann et al. [9] by solving with full
homographies (rather than affinities) in order to correct perspective,
preserving linearity by working in log-homography space. We also construct crop
constraints that preserve field-of-view; model the problem as a quadratic
(rather than linear) program to allow for an $\ell_2$ term encouraging fidelity
to the original trajectory; and add constraints and objectives to reduce
distortion. Furthermore, we propose new methods for handling salient objects
via both inclusion constraints and centering objectives. Finally, we describe a
windowing strategy to approximate the solution in linear time and bounded
memory. Our method is computationally efficient, running at 300fps on an iPhone
XS, and yields high-quality results, as we demonstrate with a collection of
stabilized videos, quantitative and qualitative comparisons to [9] and other
methods, and an ablation study.
