Track-MT3：一种基于Transformer的新型多目标跟踪算法

陈辉; 杜双燕; 连峰; 韩崇昭

doi:10.12000/JR24164

Track-MT3：一种基于Transformer的新型多目标跟踪算法

DOI: 10.12000/JR24164

陈辉^1, ,,
杜双燕¹,
连峰²,
韩崇昭²

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

基金项目: 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

详细信息

作者简介:
陈　辉，教授，博士生导师，主要研究方向为数据融合、统计信号处理、机器学习和智能决策

杜双燕，硕士生，主要研究方向为深度学习和雷达目标跟踪

连　峰，教授，博士生导师，主要研究方向为多源信息融合、滤波与估计算法、气动融合算法

韩崇昭，教授，博士生导师，主要研究方向为数据融合、电子对抗、雷达目标跟踪等

通讯作者:
陈辉 chenh@lut.edu.cn

责任主编：李天成 Corresponding Editor: LI Tiancheng
中图分类号: TP389.1
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- 被引次数: 0
出版历程
- 收稿日期: 2024-08-15
- 修回日期: 2024-10-11
- 网络出版日期: 2024-11-01

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

1.
School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2.
School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Funds: The National Natural Science Foundation of China (62163023, 61873116, 62363023, 62366031), The Key Talent Project of Gansu Province in 2024

More Information

Corresponding author: CHEN Hui, chenh@lut.edu.cn

摘要

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。
- 多目标跟踪 /
- 数据关联 /
- Transformer /
- 长时跟踪 /
- 注意力机制
Abstract: 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.
- Multitarget Tracking (MTT) /
- Data Association (DA) /
- Transformer /
- Long-term tracking /
- Attention mechanism

HTML全文

图 1 Transformer编码器

Figure 1. Transformer encoder

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图 2 改进的Transformer解码器

Figure 2. Improved Transformer decoder

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图 3 Track-MT3模型架构示意图

Figure 3. Schematic diagram of Track-MT3 model architecture

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图 4 检测查询和跟踪查询示意图

Figure 4. Schematic diagram of detection query and track query

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图 5 查询变换与时间特征编码模块

Figure 5. Query transformation and temporal feature encoding module

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图 6 训练损失函数曲线

Figure 6. Training loss function curve

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图 7 一个滑动窗口下模型的输入和输出

Figure 7. Inputs and outputs of the model under a sliding window

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图 8 编码器输出数据分析可视化

Figure 8. Visualisation of the analysis of the encoder output data

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图 9 查询向量和编码器输出的注意力分数可视化

Figure 9. Attention score visualisation of query vectors and encoder outputs

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图 10 不同实验场景下的轨迹跟踪图

Figure 10. Trajectory tracking plots for different experimental scenarios

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图 11 不同实验场景下目标数量变化图

Figure 11. Variation of the number of targets in different experimental scenarios

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图 12 不同场景下评价指标对比

Figure 12. Comparison of evaluation indicators in different scenarios

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图 13 查询置信度阈值稳健性分析

Figure 13. Robustness analysis of query confidence threshold

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图 14 鲁棒性测试

Figure 14. Robustness test

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表 1 训练样本信息

Table 1. Training sample information

参数	数值
总的样本数(有效量测点数)	401651991
真实目标量测点数	81664937
杂波量测点数	319987054
平均每个批次样本总数	8034
平均每个时间窗口样本总数	252

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表 2 实验环境

Table 2. Experimental environment

项目	版本
CPU	12th Gen Intel(R) Core i5-12400
GPU	NVIDIA GeForce RTX 3090 Ti
Python	3.7.4
Pytorch	1.6.0
Torchvision	0.7.0
CUDA	4.14.0

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表 3 Track-MT3网络参数

Table 3. Track-MT3 network parameters

参数	取值
编码器层数	6
解码器层数	6
编码器输入数据维度	256
解码器输入数据层数	256
多头注意力头数	8
查询向量数量	16
前馈网络隐藏层维度	2048
神经元Dropout	0.1
预测器MLP层数	3
预测器隐藏层维度	128

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表 4 模型训练参数

Table 4. Model training parameters

参数	取值
优化器	Adam
Epoch数	50000
Batch Size	32
初始学习率	0.0002
学习率衰减容忍度	5000
学习率衰减因子	0.5

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表 5 不同仿真场景参数设置

Table 5. Parameter settings for different simulation scenarios

场景	目标数量(个)	出生率	死亡率
场景1	6	0.04	0.01
场景2	6	0.08	0.02
场景3	10	0.12	0.03

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表 6 跟踪准确性对比

Table 6. Tracking accuracy comparison

跟踪方法	定位误差	漏检误差	虚警误差
JPDA	0.1629	0.6208	4.2812
MHT	0.6006	1.5921	3.8717
Track-MT3	0.0588	2.3683	2.3708

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表 7 计算效率对比

Table 7. Computational efficiency comparison

跟踪方法	单帧运行时间(s)	平均内存占用(MB)
JPDA	0.0041	169.6641
MHT	0.1714	209.8398
Track-MT3	0.0123	253.6656

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表 8 QTM消融实验

Table 8. QTM ablation experiment

评价指标	Full	No-QTM
GOSPA (×10^–1 m)	3.546362	4.760920
Pro-GOSPA (×10^–1 m)	1.340019	1.925471

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表 9 实验参数设置

Table 9. Experimental parameter settings

实验组	${P_{\mathrm{D}}}$	${\sigma _{\mathrm{q}}}$	${\sigma _{\mathrm{r}}}$	${\lambda _{\mathrm{c}}}$
实验1	0.95	0.01	0.1	5
实验2	0.90	0.02	0.9	10
实验3	0.85	0.03	2.0	15

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