Robust motion estimation for night-shooting videos using dual-accumulated constraint warping

Abstract

This paper introduces a novel concept of dual-accumulated constraint projection warping, as a robust and efficient motion estimation solution for night video stabilization. Small imaging-sensors used in compact hand-held cameras become very prone to noise and blur under low illumination condition. Restricted lighting results in dark boundaries and degrades textural information of the frame. Presence of these combined textural artifacts makes night-shooting a hard problem for accurate motion estimation. At poor lighting, local intensity variations result in failure of inter-frame feature or block matching correspondence. In the proposed technique, use of projection ensures accuracy under local perturbations, noise and blur conditions, while dual-accumulation eliminates the effect of dark-regions adding robustness to night-shooting condition. Efficiency of the proposed algorithm over the existing motion estimation techniques is tested and verified over different categories of night shooting videos. In addition to night video stabilization the proposed scheme also performs well under normal illumination.