This article proposes a practical end-to-end neural network framework to detect tiny moving vehicles in satellite videos with low imaging quality. Some instability factors, such as illumination changes, motion blurs, and low contrast to the cluttered background, make it difficult to distinguish true objects from noise and other point-shaped distractors. Moving vehicle detection in satellite videos can be carried out based on background subtraction or frame differencing. However, these methods are prone to produce lots of false alarms and miss many positive targets. Appearance-based detection can be an alternative but is not well-suited since classifier models are of weak discriminative power for the vehicles in top view at such low resolution. This article addresses these issues by integrating motion information from adjacent frames to facilitate the extraction of semantic features and incorporating the transformer to refine the features for key points estimation and scale prediction. Our proposed model can well identify the actual moving targets and suppress interference from stationary targets or background. The experiments and evaluations using satellite videos show that the proposed approach can accurately locate the targets under weak feature attributes and improve the detection performance in complex scenarios.

中文翻译：

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卫星视频中的极低分辨率移动车辆检测

本文提出了一种实用的端到端神经网络框架，用于检测卫星视频中成像质量较低的微小移动车辆。一些不稳定因素，例如照明变化、运动模糊以及与杂乱背景的低对比度，使得很难将真实物体与噪声和其他点状干扰物区分开来。卫星视频中的移动车辆检测可以基于背景减法或帧差进行。然而，这些方法容易产生大量的误报并错过许多积极的目标。基于外观的检测可以是一种替代方法，但不是很适合，因为分类器模型在如此低的分辨率下对俯视图中的车辆的判别能力较弱。本文通过整合来自相邻帧的运动信息以促进语义特征的提取并结合转换器来改进关键点估计和尺度预测的特征，从而解决了这些问题。我们提出的模型可以很好地识别实际的移动目标并抑制来自静止目标或背景的干扰。使用卫星视频进行的实验和评估表明，该方法可以准确定位弱特征属性下的目标，提高复杂场景下的检测性能。