Investigation of moving objects through atmospheric turbulence from a non-stationary platform
This work addresses a specific challenge in computer vision for applications like surveillance or remote sensing, but it is incremental as it builds on prior methods.
The authors tackled the problem of extracting optical flow for moving objects from image sequences affected by atmospheric turbulence and captured by a moving camera, achieving results compared against ground truth.
In this work, we extract the optical flow field corresponding to moving objects from an image sequence of a scene impacted by atmospheric turbulence \emph{and} captured from a moving camera. Our procedure first computes the optical flow field and creates a motion model to compensate for the flow field induced by camera motion. After subtracting the motion model from the optical flow, we proceed with our previous work, Gilles et al~\cite{gilles2018detection}, where a spatial-temporal cartoon+texture inspired decomposition is performed on the motion-compensated flow field in order to separate flows corresponding to atmospheric turbulence and object motion. Finally, the geometric component is processed with the detection and tracking method and is compared against a ground truth. All of the sequences and code used in this work are open source and are available by contacting the authors.