用分层架构实现在嵌入式设备上高帧率追踪小无人机
SDG-Track: A Heterogeneous Observer-Follower Framework for High-Resolution UAV Tracking on Embedded Platforms
- 分观察-跟随双流设计，高低频协同处理
- 1080p下保持97.2%检测精度，达35.1帧/秒
- 无需训练的双重恢复机制应对遮挡与干扰
在边缘设备上实时追踪小型无人机面临分辨率与速度的根本矛盾：将高分辨率图像下采样至标准检测输入尺寸会导致小目标特征消失；而在资源受限平台直接处理原生1080p帧又难以满足平滑云台控制所需的吞吐量。本文提出SDG-Track，一种稀疏检测引导的追踪框架，采用观察-跟随架构解决该矛盾。观察流在GPU上以低频运行高容量检测器，从1920×1080帧中生成精准位置锚点；跟随流在CPU上通过区域约束的稀疏光流实现高频轨迹插值。为应对遮挡或光谱相似干扰导致的跟踪失败，引入无训练的双空间恢复机制，结合颜色直方图匹配与几何一致性约束。在地面到空中追踪站上的实验表明，系统实现35.1 FPS的吞吐量，同时保留97.2%的逐帧检测精度。系统已在NVIDIA Jetson Orin Nano上成功追踪真实场景中的敏捷FPV无人机。代码已公开于https://github.com/Jeffry-wen/SDG-Track。
原文摘要 · Abstract (English)
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 https://github.com/Jeffry-wen/SDG-Track
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