Error Bounded Foreground and Background Modeling for Moving Object Detection in Satellite Videos

TL;DR

This paper introduces E-LSD, an extension of low-rank and structured sparse decomposition with bounded errors, to improve moving object detection in satellite videos where traditional methods struggle due to poor resolution and low contrast.

Contribution

It proposes a novel decomposition model that explicitly handles residual errors, enhancing detection accuracy in challenging satellite video conditions.

Findings

E-LSD outperforms state-of-the-art methods in satellite video object detection.

The method effectively models residual errors, improving background subtraction.

Experimental results show increased detection precision in low-contrast, low-resolution satellite videos.

Abstract

Detecting moving objects from ground-based videos is commonly achieved by using background subtraction techniques. Low-rank matrix decomposition inspires a set of state-of-the-art approaches for this task. It is integrated with structured sparsity regularization to achieve background subtraction in the developed method of Low-rank and Structured Sparse Decomposition (LSD). However, when this method is applied to satellite videos where spatial resolution is poor and targets' contrast to the background is low, its performance is limited as the data no longer fits adequately either the foreground structure or the background model. In this paper, we handle these unexplained data explicitly and address the moving target detection from space as one of the pioneer studies. We propose a technique by extending the decomposition formulation with bounded errors, named Extended Low-rank and Structured Sparse Decomposition (E-LSD). This formulation integrates low-rank background, structured sparse foreground and their residuals in a matrix decomposition problem. We provide an effective solution by introducing an alternative treatment and adopting the direct extension of Alternating Direction Method of Multipliers (ADMM). The proposed E-LSD was validated on two satellite videos, and experimental results demonstrate the improvement in background modeling with boosted moving object detection precision over state-of-the-art methods.