Robust Motion Compensation for Event Cameras With Smooth Constraint

Abstract

Event cameras capture brightness changes at an ultra-high speed and output a series of asynchronous events. In order to form image frames without motion blur from event stream, motion compensation needs to be applied. Unlike other algorithms which either estimate ego-motion or estimate optical flow with strict assumptions, we directly align the curved event trajectories with time-varying motion parameters. By maximizing the image energy with smooth constraint on motion parameters, our algorithm can perform accurately and robustly. We conduct extensive experiments on several public datasets and the recently released event camera, i.e. CeleX-5, the experimental results verify the superiority of the proposed algorithm.