计算机工程2025，Vol.51Issue(4)：57-65,9.DOI:10.19678/j.issn.1000-3428.0069100
基于深度学习的多无人机多目标跟踪
Multi-UAV Multi-Object Tracking Based on Deep Learning
摘要
Abstract
Unmanned Aerial Vehicle(UAV)Multi-Object Tracking(MOT)technology is widely used in various fields such as traffic operation,safety monitoring,and water area inspection.However,existing MOT algorithms are primarily designed for single-UAV MOT scenarios.The perspective of a single-UAV typically has certain limitations,which can lead to tracking failures when objects are occluded,thereby causing ID switching.To address this issue,this paper proposes a Multi-UAV Multi-Object Tracking(MUMTTrack)algorithm.The MUMTTrack network adopts an MOT paradigm based on Tracking By Detection(TBD),utilizing multiple UAVs to track objects simultaneously and compensating for the perspective limitations of a single-UAV.Additionally,to effectively integrate the tracking results from multiple UAVs,an ID assignment strategy and an image matching strategy are designed based on the Speeded Up Robust Feature(SURF)algorithm for MUMTTrack.Finally,the performance of MUMTTrack is compared with that of existing widely used single-UAV MOT algorithms on the MDMT dataset.According to the comparative analysis,MUMTTrack demonstrates significant advantages in terms of MOT performance metrics,such as the Identity F1(IDF1)value and Multi-Object Tracking Accuracy(MOTA).关键词
无人机/遮挡目标/多无人机跟踪/多目标跟踪/目标关联Key words
Unmanned Aerial Vehicle(UAV)/occluded object/multi-UAV tracking/Multi-Object Tracking(MOT)/object association分类
计算机与自动化引用本文复制引用
周翰祺,方东旭,张宁波,孙文生..基于深度学习的多无人机多目标跟踪[J].计算机工程,2025,51(4):57-65,9.基金项目
国家自然科学基金(62071069). （62071069）
