DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos
This addresses tracking robustness for satellite video analysis, which is incremental as it builds on existing detection-and-tracking methods with specific enhancements for motion perturbations.
The paper tackled the problem of multi-object tracking in unstabilized satellite videos by proposing DeTracker, a framework that decouples platform motion from object motion, achieving 61.1% MOTA on a simulated dataset and 47.3% MOTA on real data.
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.