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

作　　者：周翰祺 方东旭 张宁波[1] 孙文生[1] ZHOU Hanqi;FANG Dongxu;ZHANG Ningbo;SUN Wensheng(School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100083,China;China Mobile Communications Group Chongqing Co.,Ltd.,Chongqing 401121,China)

机构地区：[1]北京邮电大学信息与通信工程学院,北京100083 [2]中国移动通信集团重庆有限公司,重庆401121

出　　处：《计算机工程》2025年第4期57-65,共9页Computer Engineering

基　　金：国家自然科学基金(62071069)。

摘　　要：无人机(UAV)多目标跟踪技术在交通运营、安全监测、水域巡检等领域受到广泛关注。然而,目前已有的多目标跟踪算法多用于单无人机多目标跟踪,而单无人机的视角通常具有一定的局限性,当目标被遮挡时目标发生ID切换会导致跟踪失败。为了解决该问题,提出一种多无人机多目标跟踪(MUMTTrack)算法。采用基于检测的跟踪(TBD)范式,利用多架无人机同时跟踪目标,弥补单无人机视角的局限性。为了有效融合多架无人机的跟踪结果,为MUMTTrack设计一种基于加速鲁棒特征(SURF)算法的图像匹配策略和ID分配策略。将MUMTTrack算法的性能与当前主流的单无人机多目标跟踪算法在MDMT数据集上进行实验比较。实验结果表明,MUMTTrack算法在识别F1(IDF1)值和多目标跟踪精度(MOTA)这两个多目标跟踪性能指标上均表现出明显的优势。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 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]