Track-MT3:一种基于Transformer的新型多目标跟踪算法  

Track-MT3:A Novel Multitarget Tracking Algorithm Based on Transformer Network

作　　者：陈辉[1] 杜双燕 连峰[2] 韩崇昭[2] CHEN Hui;DU Shuangyan;LIAN Feng;HAN Chongzhao(School of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;School of Automation Science and Engineering,Xi’an Jiaotong University,Xi’an 710049,China)

机构地区：[1]兰州理工大学电气工程与信息工程学院,兰州730050 [2]西安交通大学自动化科学与工程学院,西安710049

出　　处：《雷达学报（中英文）》2024年第6期1202-1219,共18页Journal of Radars

基　　金：国家自然科学基金(62163023,61873116,62363023,62366031);2024年甘肃省重点人才项目资助。

摘　　要：针对复杂环境中多目标跟踪数据关联难度大、难以实现目标长时间稳定跟踪的问题,该文创新性地提出了一种基于Transformer网络的端到端多目标跟踪模型Track-MT3。首先,引入了检测查询和跟踪查询机制,隐式地执行量测-目标的数据关联并且实现了目标的状态估计任务。然后,采用跨帧目标对齐策略增强跟踪轨迹的时间连续性。同时,设计了查询变换与时间特征编码模块强化目标运动建模能力。最后,在模型训练中采用了集体平均损失函数,实现了模型性能的全局优化。通过构造多种复杂的多目标跟踪场景,并利用多重性能指标进行评估,Track-MT3展现了优于MT3等基线方法的长时跟踪性能,与JPDA和MHT方法相比整体性能分别提高了6%和20%,能够有效挖掘时序信息,在复杂动态环境下实现稳定、鲁棒的多目标跟踪。To address the challenges associated with the data association and stable long-term tracking of multiple targets in complex environments,this study proposes an innovative end-to-end multitarget tracking model called Track-MT3 based on a transformer network.First,a dual-query mechanism comprising detection and tracking queries is introduced to implicitly perform measurement-to-target data association and enable accurate target state estimation.Subsequently,a cross-frame target alignment strategy is employed to enhance the temporal continuity of tracking trajectories,ensuring consistent target identities across frames.In addition,a query transformation and temporal feature encoding module is designed to improve target motion pattern modeling by adaptively combining target dynamics information at different time scales.During model training,a collective average loss function is adopted to achieve the global optimization of tracking performance,considering the entire tracking process in an end-to-end manner.Finally,the performance of Track-MT3 is extensively evaluated under various complex multitarget tracking scenarios using multiple metrics.Experimental results demonstrate that Track-MT3 exhibits superior long-term tracking performance than baseline methods such as MT3.Specifically,Track-MT3 achieves overall performance improvements of 6%and 20%against JPDA and MHT,respectively.By effectively exploiting temporal information,Track-MT3 ensures stable and robust multitarget tracking in complex dynamic environments.

关 键 词：多目标跟踪 数据关联 TRANSFORMER 长时跟踪 注意力机制 

分 类 号：TN953.6[电子电信—信号与信息处理] TP389.1[电子电信—信息与通信工程]