Abstract
Satellite videos provide continuous observations of surface dynamics but pose significant challenges for multi-object tracking (MOT), especially under unstabilized conditions where platform jitter and the weak appearance of tiny objects jointly degrade tracking performance. To address this problem, we propose DeTracker, a joint detection-and-tracking framework tailored for unstabilized satellite videos. DeTracker introduces a Global--Local Motion Decoupling (GLMD) module that explicitly separates satellite platform motion from true object motion through global alignment and local refinement, leading to improved trajectory stability and motion estimation accuracy. In addition, a Temporal Dependency Feature Pyramid (TDFP) module is developed to perform cross-frame temporal feature fusion, enhancing the continuity and discriminability of tiny-object representations. We further construct a new benchmark dataset, SDM-Car-SU, which simulates multi-directional and multi-speed platform motions to enable systematic evaluation of tracking robustness under varying motion perturbations. Extensive experiments on both simulated and real unstabilized satellite videos demonstrate that DeTracker significantly outperforms existing methods, achieving 61.1% MOTA on SDM-Car-SU and 47.3% MOTA on real satellite video data.
Abstract (translated)
卫星视频提供了对地表动态的连续观测，但在未稳定条件下进行多目标跟踪（MOT）时面临重大挑战。平台抖动和小目标外观微弱的问题共同导致了跟踪性能下降。为了应对这一问题，我们提出了DeTracker，这是一种专门针对未稳定的卫星视频设计的检测与追踪框架。DeTracker引入了一个全局-局部运动解耦(GLMD)模块，通过全局对齐和局部细化来明确区分卫星平台运动和真实目标运动，从而提高轨迹稳定性和运动估计精度。此外，还开发了一种时序依赖特征金字塔(TDFP)模块，用于跨帧的时序特征融合，增强小对象表示的一致性与可识别性。 我们进一步构建了一个新的基准数据集SDM-Car-SU，该数据集模拟了多方向和多种速度下的平台运动，从而能够在不同的运动扰动下系统地评估跟踪鲁棒性。在模拟的和真实的未稳定卫星视频上的广泛实验表明，DeTracker显著优于现有的方法，在SDM-Car-SU数据集中达到了61.1% MOTA（多项评估追踪准确度），而在真实卫星视频数据集中的表现也达到47.3% MOTA。
URL
https://arxiv.org/abs/2601.09240
