UAV Target Tracking Based on Multi-Feature Fusion and Memory Re-Detection

ZHU Kaifeng; WU Danfeng; ZHU Jihong; ZHOU Fenfen; JIN Shuang; KUANG Minchi

doi:10.12382/bgxb.2026.0036

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UAV Target Tracking Based on Multi-Feature Fusion and Memory Re-Detection

更新时间：2026-08-12

- UAV Target Tracking Based on Multi-Feature Fusion and Memory Re-Detection
- Acta Armamentarii (2026)
- 作者机构：
  
  1.北京联合大学北京市信息服务工程重点实验室, 北京 100101
  2.北京联合大学机器人学院 , 北京 100101
  3.清华大学精密仪器系 , 北京 100084
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2026.0036
  CLC： TJ301
- Received：11 January 2026，
  
  Revised：2026-03-15，
  
  Accepted：14 May 2026，
- 稿件说明：
移动端阅览
ZHU Kaifeng, WU Danfeng, ZHU Jihong, et al. UAV Target Tracking Based on Multi-Feature Fusion and Memory Re-Detection[J/OL]. Acta Armamentarii, 2026.
DOI：

ZHU Kaifeng, WU Danfeng, ZHU Jihong, et al. UAV Target Tracking Based on Multi-Feature Fusion and Memory Re-Detection[J/OL]. Acta Armamentarii, 2026. DOI： 10.12382/bgxb.2026.0036.

摘要

针对无人机目标跟踪中目标尺度与视角变化、遮挡以及目标丢失后再出现等复杂情形，提出一种增强型核相关滤波跟踪框架。通过构建尺度金字塔并引入旋转不变描述子，实现对目标尺度与视角变化的协同感知；结合基于空间正则化的抗遮挡模块，提高遮挡条件下的跟踪鲁棒性；融合基于卡尔曼滤波的记忆引导重检测与局部搜索策略，增强目标丢失后的恢复能力。在UAV123与OTB100数据集上的实验结果表明：所提方法在资源受限平台上分别可实现33.21 帧/s、35.39 帧/s的实时处理性能，并分别获得54.6%/66.0%的定位精度和37.6%/55.8%的成功率；该方法在目标外观变化与短时遮挡场景下具有良好的稳定性，并能够在目标丢失后实现快速恢复，具备工程应用价值。

Abstract

To address the challenges of target scale and viewpoint variations

occlusion

and target reappearance after loss in unmanned aerial vehicle target tracking

an enhanced kernelized correlation filter tracking framework is proposed. The framework constructs a scale pyramid and incorporates rotation-invariant descriptors to achieve collaborative perception of target scale and viewpoint variations; a spatial regularization-based anti-occlusion module is integrated to improve tracking robustness under occlusion; and a memory-guided re-detection strategy combined with Kalman filtering and local search is employed to enhance recovery capability after target loss. Experimental results on the UAV123 and OTB100 datasets demonstrate that the proposed method achieves real-time performance of 33.21/35.39 FPS on resource-constrained platforms

with localization accuracies of 54.6%/66.0% and success rates of 37.6%/55.8%

respectively. The results indicate that the proposed approach maintains stable tracking performance under target appearance variations and short-term occlusion

and is capable of rapid recovery after target loss

showing strong potential for practical engineering applications.

关键词

Keywords

references

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