| Zhang Qijia (张琪佳),Qin Danyang,Zhang Xiao.[J].高技术通讯(英文),2026,32(1):60~72 |
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| Optimization of UAV visual tracking based on structure-consistent modeling in complex environments |
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| DOI：10. 3772 / j. issn. 1006-6748. 2026. 01. 007 |
| 中文关键词: |
| 英文关键词: adaptive anchor proposal, meta updater, temporal memory bank, UAV tracking. |
| 基金项目: |
| Author Name | Affiliation | | Zhang Qijia (张琪佳) | (Key laboratory of Electronic and Communication Engineering, Heilongjiang University, Harbin 150006, P. R. China) | | Qin Danyang | | | Zhang Xiao | |
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| 中文摘要: |
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| 英文摘要: |
| This paper investigates the challenges of structural inconsistency, matching accuracy degradation, and trajectory interruptions caused by high-speed motion, frequent occlusions, and appearance variations of unmanned aerial vehicle (UAV) targets in low-altitude airspace. A novel UAV visual tracking method is proposed for dynamic structural distortions, with a focus on structural consistency modeling to improve system robustness in complex scenarios. Unlike prior methods such as STARK,which rely on spatio-temporal prediction, and KeepTrack, which emphasize template maintenance,our approach enforces structural-level consistency between historical and current features, thereby addressing UAV-specific issues of rapid maneuvering and environmental complexity. The proposed framework features a structure-aware architecture that incorporates dual complementary mechanisms serving as spatial completion and temporal restoration components. First, a multi-scale structure extraction module with adaptive anchor scheduling is developed to dynamically perceive spatial target shape and generate high-quality proposals. Second, a structural memory module is designed to reconstruct local regions by leveraging high-confidence historical structural representations, thereby maintaining spatiotemporal coherence across frames. Furthermore, a structural verification mechanism coupled with a meta-learning-driven re-identification strategy is introduced to detect abrupt structural distortions and adaptively update templates, significantly improving resilience against disturbances. Overall, the main contributions of this paper can be summarized as follows: (1) introducing structural consistency modeling into UAV visual tracking for the first time; (2) designing a unified framework that combines adaptive proposal generation, full-image matching, and re-identification under structural constraints; and (3) achieving state-of-the-art performance on the anti-UAV benchmark, highlighting the method’s practical value in real-world UAV surveillance applications. |
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