Hyperlapse generation of omnidirectional videos by adaptive sampling based on 3D camera positions

Capturing a city with an omnidirectional camera can produce a continuous motion street view and provides the view of the moving person. However, watching such a video is not necessarily pleasant because the videos are often excessively lengthy. In this paper, we propose a method for shortening an omnidirectional video. Subsampling a video captured by a hand-held camera displays a significant amount of destabilization resulting from shaking. This prompted us to propose an adaptive subsampling scheme that selects optimal frames by minimizing our cost function based on 3D camera positions. This optimal selection suppresses only the translational camera instabilities. The rotational instabilities are initially ignored and later compensated for. This approach allowed us to successfully generate a subsampled and stable omnidirectional video. In addition, we propose an alternative measure of translational instability to evaluate our frame selection.