Detection of moving objects through turbulent media. Decomposition of Oscillatory vs Non-Oscillatory spatio-temporal vector fields

TL;DR

This paper introduces a novel method for detecting moving objects in turbulent atmospheric conditions by extending 2D decomposition algorithms to 3D vector fields using curvelet spaces, effectively distinguishing turbulence from actual movement.

Contribution

It presents a new 3D vector field decomposition approach based on curvelet spaces to differentiate between turbulence-induced motion and real object movement.

Findings

Effective separation of turbulence and object movement demonstrated on real data

Extension of 2D cartoon+texture decomposition to 3D vector fields

Improved detection accuracy in turbulent atmospheric conditions

Abstract

In this paper, we investigate how moving objects can be detected when images are impacted by atmospheric turbulence. We present a geometric spatio-temporal point of view to the problem and show that it is possible to distinguish movement due to the turbulence vs. moving objects. To perform this task, we propose an extension of 2D cartoon+texture decomposition algorithms to 3D vector fields. Our algorithm is based on curvelet spaces which permit to better characterize the movement flow geometry. We present experiments on real data which illustrate the efficiency of the proposed method.