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.

翻译：卫星视频能够提供对地表动态的连续观测，但对多目标跟踪（MOT）提出了重大挑战，尤其是在非稳定条件下，平台抖动与微小目标外观特征微弱共同导致跟踪性能下降。为解决此问题，我们提出了DeTracker，一个专为非稳定卫星视频设计的联合检测与跟踪框架。DeTracker引入了一个全局-局部运动解耦（GLMD）模块，通过全局对齐与局部细化，显式地将卫星平台运动与真实目标运动分离，从而提升了轨迹稳定性与运动估计精度。此外，我们开发了一个时序依赖特征金字塔（TDFP）模块，用于执行跨帧时序特征融合，增强了微小目标表征的连续性与可区分性。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、多速度的平台运动，以支持在不同运动扰动下对跟踪鲁棒性进行系统性评估。在模拟和真实非稳定卫星视频上进行的大量实验表明，DeTracker显著优于现有方法，在SDM-Car-SU上达到61.1%的MOTA，在真实卫星视频数据上达到47.3%的MOTA。