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

arXiv: https://arxiv.org/abs/1908.09539 · HTML: https://mlsanity.com/paper/1908.09539/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 8 of 8)

- [Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark](https://mlsanity.com/paper/2111.12960/) (2021)
- [Vehicle Perception from Satellite](https://mlsanity.com/paper/2402.00703/) (2024)
- [Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos](https://mlsanity.com/paper/2411.15895/) (2024)
- [SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite Videos](https://mlsanity.com/paper/2412.18214/) (2024)
- [Deep Vehicle Detection in Satellite Video](https://mlsanity.com/paper/2204.06828/) (2022)
- [DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos](https://mlsanity.com/paper/2601.09240/) (2026)
- [DiffMOD: Progressive Diffusion Point Denoising for Moving Object Detection in Remote Sensing](https://mlsanity.com/paper/2504.10278/) (2025)
- [Learn Temporal Consistency For Robust Satellite Video Detector](https://mlsanity.com/paper/2606.15112/) (2026)

## Abstract (arXiv)

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.