# SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite Videos

arXiv: https://arxiv.org/abs/2412.18214 · HTML: https://mlsanity.com/paper/2412.18214/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark](https://mlsanity.com/paper/2111.12960/) (2021)
- [Error Bounded Foreground and Background Modeling for Moving Object Detection in Satellite Videos](https://mlsanity.com/paper/1908.09539/) (2019)
- [Image Segmentation in Video Sequences: A Probabilistic Approach](https://mlsanity.com/paper/1302.1539/) (2013)
- [Online Structured Sparsity-based Moving Object Detection from Satellite Videos](https://mlsanity.com/paper/1911.12989/) (2019)

## Built on by (most cited first, 2 of 2)

- [DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos](https://mlsanity.com/paper/2601.09240/) (2026)
- [SGMA: Semantic-Guided Modality-Aware Segmentation for Remote Sensing with Incomplete Multimodal Data](https://mlsanity.com/paper/2603.02505/) (2026)

## Abstract (arXiv)

Vehicle detection and tracking in satellite video is essential in remote sensing (RS) applications. However, upon the statistical analysis of existing datasets, we find that the dim vehicles with low radiation intensity and limited contrast against the background are rarely annotated, which leads to the poor effect of existing approaches in detecting moving vehicles under low radiation conditions. In this paper, we address the challenge by building a Small and Dim Moving Cars (SDM-Car) dataset with a multitude of annotations for dim vehicles in satellite videos, which is collected by the Luojia 3-01 satellite and comprises 99 high-quality videos. Furthermore, we propose a method based on image enhancement and attention mechanisms to improve the detection accuracy of dim vehicles, serving as a benchmark for evaluating the dataset. Finally, we assess the performance of several representative methods on SDM-Car and present insightful findings. The dataset is openly available at https://github.com/TanedaM/SDM-Car.