面向不同挑战及同异质信息分离的RGBT跟踪

方鑫; 陈柘; 刘占文; 李小鹏; 宿雨心

doi:10.12263/DZXB.20240713

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面向不同挑战及同异质信息分离的RGBT跟踪

学术论文 | 更新时间：2025-12-08

- 面向不同挑战及同异质信息分离的RGBT跟踪
- Facing Different Challenges and Separating Homogeneous and Heterogeneous Information for RGBT Tracking
- 电子学报 2025年53卷第3期页码：910-925
- 作者机构：
  
  长安大学信息工程学院，陕西西安 710064
- 作者简介：
  
  [ "方鑫男，2000年出生于陕西省商洛市.现为长安大学信息工程学院硕士研究生.主要研究方向为视觉目标跟踪. E-mail: fangxin@chd.edu.cn" ]
  [ "陈柘男，1969年出生于陕西省西安市.博士，现为长安大学信息工程学院副教授、硕士生导师.主要研究方向为计算机视觉与机器学习.E-mail: zchen@chd.edu.cn" ]
  [ "刘占文女，1983年出生于河南省安阳市.博士，现为长安大学信息工程学院教授、博士生导师.主要研究方向为车路云一体化交通环境感知与在环测试.E-mail: zwliu@chd.edu.cn" ]
  [ "李小鹏男，2000年出生于福建省福州市.现为长安大学信息工程学院硕士研究生.主要研究方向为图像超分辨率. E-mail: xiaopengli@chd.edu.cn" ]
  [ "宿雨心女，1999年出生于内蒙古自治区呼和浩特市.现为长安大学信息工程学院硕士研究生.主要研究方向为图像复原.E-mail: yuxinsu@chd.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(52172302);陕西省重点研发计划项目(2022GY-063)
- DOI：10.12263/DZXB.20240713
  中图分类号： TP391.4;
- 收稿：2024-07-31，
  
  修回：2025-02-26，
  
  纸质出版：2025-03-25
- 稿件说明：
移动端阅览
方鑫, 陈柘, 刘占文, 等. 面向不同挑战及同异质信息分离的RGBT跟踪[J]. 电子学报, 2025, 53(03): 910-925.

FANG Xin, CHEN Zhe, LIU Zhan-wen, et al. Facing Different Challenges and Separating Homogeneous and Heterogeneous Information for RGBT Tracking[J]. Acta Electronica Sinica, 2025, 53(03): 910-925.
方鑫, 陈柘, 刘占文, 等. 面向不同挑战及同异质信息分离的RGBT跟踪[J]. 电子学报, 2025, 53(03): 910-925. DOI：10.12263/DZXB.20240713

FANG Xin, CHEN Zhe, LIU Zhan-wen, et al. Facing Different Challenges and Separating Homogeneous and Heterogeneous Information for RGBT Tracking[J]. Acta Electronica Sinica, 2025, 53(03): 910-925. DOI：10.12263/DZXB.20240713

摘要

可见光热红外（RGB and Thermal infrared，RGBT）跟踪是一种结合了可见光和热红外光两种不同传感器信息的多模态目标跟踪方法.这种方法旨在克服单一传感器在特定环境下的局限性，通过融合多种传感器的数据来提高目标跟踪的鲁棒性和准确性.然而，在现有的RGBT跟踪算法中，大多将可见光与热红外图像提取的特征直接进行融合，忽略了两种模态间的同质性与异质性.此外，RGBT跟踪还经常受到目标快速运动、尺度变化、光照变化、热交叉和遮挡等多种挑战因素的影响，现有工作往往是通过研究单一结构来同时解决所有问题，但这需要足够复杂的模型和足够多的训练数据.本文提出了一种新的面向不同挑战并结合多模态同异质信息分离与融合的网络，用于RGBT跟踪.在该网络的每层主干中都设计了一个挑战感知模块用于融合每种挑战下来自可见光与热红外两种不同模态的特征，并自适应地聚合所有挑战下的融合特征.此外，还加入了注意力增强模块及多尺度辅助模块对主干网络所提取的特征进行增强.最后根据可见光与热红外的同质性与异质性，分别提取它们的特有特征与共有特征并进行自适应融合.在GTOT、RGBT234和LasHeR数据集上的大量实验表明，与现有RGBT跟踪方法相比，本文提出的跟踪器显示出非常强的竞争力.

Abstract

RGB and Thermal infrared (RGBT) tracking is a multi-modal object tracking method that integrates different information from visible light and thermal infrared sensors. This method aims to overcome the limitations of single sensor in a specific condition and increase the robustness and accuracy of object tracking by fusing data from multiple sensors. However

the majority of RGBT tracking methods in use today directly fuse features extracted from thermal infrared and visible light images

ignoring the homogeneity and heterogeneity of the two modalities. In addition

RGBT tracking is often affected by multiple challenging factors such as objects fast motion

scale variation

illumination variation

thermal crossover

and occlusion. Existing work often focuses on a single model to solve all challenges simultaneously

which requires highly complex model and extensive training data. This paper proposes a novel network called CMHHNet (facing different Challenges and combining Multi-modal Homogeneous and Heterogeneous information separation and integration Network) for RGBT tracking. In this network

a challenge-aware module is deployed in each layer of the backbone to fuse the visible light and thermal infrared features from two different modalities under each challenge separately

and adaptively aggregate the fused features under all challenges. In addition

an attention enhancement module and a multi-scale auxiliary module are added to strengthen the features that the backbone network has extracted. Finally

according to the homogeneity and heterogeneity of thermal infrared and visible light

their unique and common features are extracted separately and adaptively fused. Extensive experiments on GTOT

RGBT234 and LasHeR datasets demonstrate that the tracker proposed in this paper shows quite strong competitiveness compared with existing RGBT tracking methods.

关键词

Keywords

references

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