Abstract
Single object tracking in satellite videos is inherently challenged by small target, blurred background, large aspect ratio changes, and frequent visual occlusions. These constraints often cause appearance-based trackers to accumulate errors and lose targets irreversibly. To systematically mitigate both spatial ambiguities and temporal information loss, we propose SiamGM, a novel geometry-aware and motion-guided Siamese network. From a spatial perspective, we introduce an Inter-Frame Graph Attention (IFGA) module, closely integrated with an Aspect Ratio-Constrained Label Assignment (LA) method, establishing fine-grained topological correspondences and explicitly preventing surrounding background noise. From a temporal perspective, we introduce the Motion Vector-Guided Online Tracking Optimization method. By adopting the Normalized Peak-to-Sidelobe Ratio (nPSR) as a dynamic confidence indicator, we propose an Online Motion Model Refinement (OMMR) strategy to utilize historical trajectory information. Evaluations on two challenging SatSOT and SV248S benchmarks confirm that SiamGM outperforms most state-of-the-art trackers in both precision and success metrics. Notably, the proposed components of SiamGM introduce virtually no computational overhead, enabling real-time tracking at 130 frames per second (FPS). Codes and tracking results are available at this https URL.
Abstract (translated)
卫星视频中的单个目标跟踪由于小尺寸目标、模糊背景、显著纵横比变化以及频繁的视觉遮挡，面临巨大挑战。这些因素常常导致基于外观的追踪器积累错误，并且不可逆转地失去目标。为了系统性地缓解空间模糊和时间信息损失的问题，我们提出了SiamGM（几何感知与运动引导的暹罗网络），这是一种新颖的方法。从空间角度来看，我们引入了帧间图注意力模块(IFGA)，并将其紧密集成到纵横比约束标签分配(LA)方法中，以建立细粒度拓扑对应关系，并明确防止周围背景噪声的影响。从时间角度来看，我们提出了运动向量引导的在线追踪优化方法。 通过采用归一化峰值侧瓣比（nPSR）作为动态置信度指标，我们提出了一种在线运动模型精炼(OMMR)策略来利用历史轨迹信息。在两个具有挑战性的SatSOT和SV248S基准测试上进行的评估表明，SiamGM在精度和成功指标方面均优于大多数最先进的追踪器。值得注意的是，SiamGM所提出的组件几乎不引入计算开销，从而实现实时跟踪速度高达130帧每秒(FPS)。 代码及跟踪结果可在以下链接获取：[此链接](https://this-url.com)（原文中提供的链接未显示，请根据实际情况访问相关资源）。
URL
https://arxiv.org/abs/2603.07564
