无线电工程2024,Vol.54Issue(9) :2162-2172.DOI:10.3969/j.issn.1003-3106.2024.09.013

多层次特征交互的双模态目标追踪算法

Multi-level Feature Interaction Dual-modal Object Tracking Algorithm

刘宜松 周冬明 晏开祥
无线电工程2024,Vol.54Issue(9) :2162-2172.DOI:10.3969/j.issn.1003-3106.2024.09.013

多层次特征交互的双模态目标追踪算法

Multi-level Feature Interaction Dual-modal Object Tracking Algorithm

刘宜松 1周冬明 1晏开祥1
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作者信息

  • 1. 云南大学信息学院,云南 昆明 650500
  • 折叠

摘要

红外与可见光(RGB and Thermal,RGBT)目标跟踪得益于可见光与热红外2种模态数据的互补优势能够很好地提升跟踪器在部分极端环境下的目标定位能力.现有工作主要集中于如何对2种模态的特征进行提取和融合,忽略了不同模态中分层深度特征的潜在价值,这些分层深度特征对目标的定位与分类有着重要的作用.为此,提出了一种多层次特征交互的多模态自适应融合目标跟踪算法(Multi-layer Feature Interaction and Modal-adaptation Fusion Network,MIMFNet),通过特征提取器和注意力机制对分层特征进行提取与自适应校准;分层特征聚合子网将不同层的特征进行自上而下相互聚合,使低层特征不仅保留了自身的空间细节也获取了高层特征的语义信息.设计了一种多模态信息传递模块对2种模态的分层信息进行自适应融合,使模型聚焦到质量更高的特征通道上.通过多个公开数据集上的大量实验结果表明,提出的多模态目标跟踪算法具有优良的抗干扰特性,特别是由于尺度变化(Scale Variation,SV)、热交叉(Thermal Crossover,TC)和遮挡(Occlusion,OCC)等因素引起的跟踪漂移得到了显著优化.

Abstract

RGBT object tracking benefits from the complementary advantages of visible RGB and thermal infrared modalities,which can effectively enhance the object localization capability of trackers in challenging environmental conditions.Existing works mainly focus on how to extract and fuse features from these two modalities,while neglecting the potential value of hierarchical deep features within each modality,which play crucial roles in object localization and classification.To address this problem,the Multi-layer Feature Interaction and Modal-adaptation Fusion Network(MIMFNet)is proposed to achieve the RGB and thermal tracking.Firstly,the algorithm extracts and adaptively calibrates hierarchical features through feature extractors and attention mechanisms.Secondly,a hierarchical feature aggregation sub-network combines features from different layers in a top-down fashion,allowing low-level features to retain their spatial details while capturing semantic information from high-level features.Finally,a multi-modal information propagation module is designed to adaptively fuse hierarchical information from both modalities to direct the model's focus towards higher-quality feature channels.Extensive experimental results on multiple publicly available datasets demonstrate the strong anti-jamming properties of the proposed RGBT tracking algorithm.In particular,significant improvements have been achieved in dealing with tracking drifts caused by factors such as Scale Variation(SV),Thermal Crossover(TC)and Occlusion(OCC).

关键词

红外与可见光目标跟踪/自适应融合/特征聚合/通道注意力

Key words

RGBT object tracking/adaptive fusion/feature aggregation/channel attention

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基金项目

国家自然科学基金(62066047)

国家自然科学基金(61966037)

云南大学专业学位研究生实践创新基金项目资助(ZC-23234092)

出版年

2024
无线电工程
中国电子科技集团公司第五十四研究所

无线电工程

影响因子:0.667
ISSN:1003-3106
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