FMA-Net: Flow-Driven Motion-Aware Network for Multiobject Tracking in Satellite Videos

Satellite video multiobject tracking (MOT) is fundamentally challenged by extremely weak inter-frame displacement. Most existing methods capture motion at the image level, limiting the effective utilization of fine-grained motion information. To address this limitation, we propose a flow-driven motion-aware network (FMA-Net) that performs pixel-level motion modeling via the designed flow-driven motion estimator (FME). The captured fine-grained motion is then fully exploited by the motion-aware fusion (MAF) module to guide inter-frame feature fusion, allowing the network to focus on truly dynamic targets during detection. The motion-aware refinement (MAR) module leverages the motion priors to maintain identity consistency and reduce mismatches during association, improving tracking robustness. By jointly modeling and exploiting fine-grained motion cues throughout both detection and association, FMA-Net significantly enhances tracking accuracy in satellite video MOT. Extensive experiments on three satellite video MOT benchmarks demonstrate that FMA-Net achieves state-of-the-art performance across diverse object categories, validating its effectiveness and generalization capability. The code will be publicly available at: https://github.com/luweiqing/FMA-Net