无人机视频多目标特征关联技术研究进展-干扰环境下的无人机多源感知”专栏

  • 伍瀚 ,
  • 孙浩 ,
  • 刘奎 ,
  • 计科峰 ,
  • 匡纲要
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  • 1. 国防科技大学电子科学学院电子信息系统复杂电磁环境效应国家重点实验室
    2. 国防科技大学

收稿日期: 2025-03-12

  修回日期: 2025-05-29

  网络出版日期: 2025-06-06

基金资助

国家自然科学基金

Multi-object feature association in UAV videos: Recent progress and perspectives

  • WU Han ,
  • SUN Hao ,
  • LIU Kui ,
  • JI Ke-Feng ,
  • KUANG Gang-Yao
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Received date: 2025-03-12

  Revised date: 2025-05-29

  Online published: 2025-06-06

摘要

无人机凭借其高度机动性和广阔的视野,在智能监控、智慧城市、态势感知和低空经济等领域展现出重要的应用价值。多目标特征关联作为无人机视觉感知的核心技术,广泛应用于多目标跟踪等任务中,通过准确关联不同视频帧的目标实现对目标的持续状态估计。然而,无人机平台的小目标占比高、目标外观和光照条件变化等因素为多目标特征关联带来了严峻的挑战。本文系统综述了无人机视频多目标特征关联技术,相比现有文献首次涵盖了多视角和多光谱的特征关联,总结了不同类型特征关联的代表性方法,并根据不同技术路线对现有方法进行分类阐述。此外,梳理了无人机视频多目标特征关联主流公开数据集,包括单视频数据集、多视角视频数据集和多光谱视频数据集,并选取VisDrone、MDMT和VT-Tiny-MOT三个代表性数据集对主流关联算法的性能进行横向对比分析。最后,总结了无人机视频特征关联亟待解决的问题并展望未来可能的研究方向,以期为无人机多目标特征关联的深入研究提供参考。

本文引用格式

伍瀚 , 孙浩 , 刘奎 , 计科峰 , 匡纲要 . 无人机视频多目标特征关联技术研究进展-干扰环境下的无人机多源感知”专栏[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2025.31967

Abstract

Unmanned Aerial Vehicles (UAVs) have demonstrated significant application potential in intelligent surveillance, smart cities, situational awareness, and the low-altitude economy, owing to their high maneuverability and expansive field of view. As a core technology in UAV-based visual perception, multi-object feature association enables continuous state estimation of multiple targets, which is widely used in tasks such as multi-object tracking. However, the high dynamic viewpoint of UAV platforms, complex background interference, a high proportion of small targets, and drastic variations in target appearance present substantial challenges for robust feature association. Therefore, this paper provides a comprehensive survey of multi-object feature association techniques for UAV videos, covering methods leveraging multi-view and multi-spectral videos for the first time compared to existing literatures. Representative approaches are systematically summarized and categorized according to their underlying technical paradigms. Furthermore, this paper reviews the major publicly available datasets for UAV-based multi-object feature association, including those designed for single UAV video, multi-drone videos, and multi-modal videos. Three representative datasets, namely, VisDrone, MDMT, and VT-Tiny-MOT, are selected for a comparative analysis of the performance of state-of-the-art feature association methods. Finally, this paper summarizes the urgent challenges in UAV feature association and prospects potential future research directions, providing valuable insights for advancing UAV multi-object feature association research.

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