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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
| Comments: | Withdrawn by the authors due to unresolved authorship and public-disclosure authorization issues |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2512.04883 [cs.CV] |
| (or arXiv:2512.04883v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2512.04883 arXiv-issued DOI via DataCite |
From: Jiawen Wen [view email]
[v1]
Thu, 4 Dec 2025 15:11:43 UTC (12,713 KB)
[v2]
Sat, 23 May 2026 03:19:07 UTC (1 KB) (withdrawn)
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