Abstract
Real-time tracking of small unmanned aerial vehicles (UAVs) on edge devices faces a fundamental resolution-speed conflict. Downsampling high-resolution imagery to standard detector input sizes causes small target features to collapse below detectable thresholds. Yet processing native 1080p frames on resource-constrained platforms yields insufficient throughput for smooth gimbal control. We propose SDG-Track, a Sparse Detection-Guided Tracker that adopts an Observer-Follower architecture to reconcile this conflict. The Observer stream runs a high-capacity detector at low frequency on the GPU to provide accurate position anchors from 1920x1080 frames. The Follower stream performs high-frequency trajectory interpolation via ROI-constrained sparse optical flow on the CPU. To handle tracking failures from occlusion or model drift caused by spectrally similar distractors, we introduce Dual-Space Recovery, a training-free re-acquisition mechanism combining color histogram matching with geometric consistency constraints. Experiments on a ground-to-air tracking station demonstrate that SDG-Track achieves 35.1 FPS system throughput while retaining 97.2\% of the frame-by-frame detection precision. The system successfully tracks agile FPV drones under real-world operational conditions on an NVIDIA Jetson Orin Nano. Our paper code is publicly available at this https URL
Abstract (translated)
实时在边缘设备上追踪小型无人驾驶飞行器（UAV）面临着基本的分辨率-速度冲突。将高分辨率图像降采样至标准检测模型输入尺寸会导致小目标特征消失到不可检出阈值以下。然而，在资源受限平台上处理原始1080p帧无法达到平稳云台控制所需的足够吞吐量。为此，我们提出了SDG-Track，即稀疏检测引导追踪器，采用观察者-跟随者架构来解决这一冲突。观察者流在GPU上以低频运行高容量检测器，从1920x1080帧中提供准确的位置锚点。跟随者流则通过CPU上的感兴趣区域（ROI）约束稀疏光流，在高频下进行轨迹插值。 为了处理由遮挡或与目标具有相似光谱特性的干扰物导致的追踪失败，我们引入了双空间恢复机制——这是一种无需训练的重新获取手段，结合颜色直方图匹配和几何一致性约束。在地面至空中跟踪站上的实验表明，SDG-Track能够在保持97.2%的帧间检测精度的同时实现35.1 FPS的系统吞吐量。该系统成功地在现实世界操作条件下，在NVIDIA Jetson Orin Nano上追踪敏捷的第一人称视角（FPV）无人机。 我们的研究代码可公开获取，详情请访问：[提供的链接] (请将 [提供的链接] 替换为实际的网址)
URL
https://arxiv.org/abs/2512.04883
