Simultaneous Video Stabilization and Moving Object Detection in Turbulence

    Authors

    O. Oreifej; X. Li;M. Shah

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    IEEE Trans. Pattern Anal. Mach. Intell.

    Keywords

    Three-term decomposition; turbulence mitigation; rank optimization; moving object detection; particle advection; restoring force; RESTORATION; COMPONENTS; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

    Abstract

    Turbulence mitigation refers to the stabilization of videos with nonuniform deformations due to the influence of optical turbulence. Typical approaches for turbulence mitigation follow averaging or dewarping 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 three-term 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 l(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 l(1) norm. Second, since the object's motion is linear and intrinsically different from the Gaussian-like turbulence, a Gaussian-based turbulence model can be employed to enforce an additional constraint on the search space of the minimization. We demonstrate the robustness of our approach on challenging sequences which are significantly distorted with atmospheric turbulence and include extremely tiny moving objects.

    Journal Title

    Ieee Transactions on Pattern Analysis and Machine Intelligence

    Volume

    35

    Issue/Number

    2

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    450

    Last Page

    462

    WOS Identifier

    WOS:000312560600016

    ISSN

    0162-8828

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