dorsal/arxiv
View SchemaDeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos
| Authors | Jiajun Chen, Jing Xiao, Shaohan Cao, Yuming Zhu, Liang Liao, Jun Pan, Mi Wang |
|---|---|
| Categories | |
| ArXiv ID | 2601.09240vv1 |
| URL | https://arxiv.org/abs/2601.09240 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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.
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"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.",
"arxiv_id": "2601.09240",
"authors": [
"Jiajun Chen",
"Jing Xiao",
"Shaohan Cao",
"Yuming Zhu",
"Liang Liao",
"Jun Pan",
"Mi Wang"
],
"categories": [
"cs.CV",
"eess.IV"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos",
"url": "https://arxiv.org/abs/2601.09240",
"version": "v1"
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