Visual Gyroscope: Combination of Deep Learning Features and Direct Alignment for Panoramic Stabilization
This work addresses stabilization for panoramic cameras in aerial applications, presenting an incremental improvement over existing methods.
The paper tackled panoramic video stabilization by combining deep learning features with direct alignment to estimate camera attitude, achieving robust and accurate results validated on aerial vehicle sequences.
In this article we present a visual gyroscope based on equirectangular panoramas. We propose a new pipeline where we take advantage of combining three different methods to obtain a robust and accurate estimation of the attitude of the camera. We quantitatively and qualitatively validate our method on two image sequences taken with a $360^\circ$ dual-fisheye camera mounted on different aerial vehicles.