Simultaneous Video Stabilization and Moving Object Detection in Turbulence

Turbulence mitigation refers to the stabilization of videos with non-uniform deformations due to the influence of optical turbulence. Typical approaches for turbulence mitigation follow averaging or de-warping techniques. Although these methods can reduce the turbulence, they distort the independently moving objects which can often be of great interest. In this paper, we address the novel problem of simultaneous turbulence mitigation and moving object detection. We propose a novel threeterm low-rank matrix decomposition approach in which we decompose the turbulence sequence into three components: the background, the turbulence, and the object. We simplify this extremely difficult problem into a minimization of nuclear norm, Frobenius norm, and `1 norm. Our method is based on two observations: First, the turbulence causes dense and Gaussian noise, and therefore can be captured by Frobenius norm, while the moving objects are sparse and thus can be captured by `1 norm. Second, since th...

• Type: Journal
• Year: 2012
• Where: PAMI
• Authors: Omar Oreifej, Xin Li, and Mubarak Shah
• Keywords: Background Subtraction, Detection, Low Rank Optimization, Particle Advection, RPCA, Tracking, Turbulence Mitigation, Video Denoising