广东工业大学学报2026，Vol.43Issue(2)：21-29,9.DOI:10.12052/gdutxb.240169
基于高效时序建模的RGB-T跟踪
Efficient Temporal Modeling for RGB-T Tracking
摘要
Abstract
RGB-Thermal(RGB-T)tracking methods utilize the complementarity of visible light and thermal infrared images to improve the accuracy of target tracking in the scenarios of low light conditions and adverse weather.However,most existing studies focus only on image-level appearance matching,making them difficult to cope with challenges of target deformation and interference under complex environments.To address this problem,a tracking method based on efficient temporal modeling is proposed.Firstly,the temporal information is modeled,and the feature fusion module is improved to process temporal information.Then,a lightweight adapter is used for fine-tuning to improve the feature extraction module for thermal infrared images,enhancing the model's ability to extract features from different modal information,reducing the computational memory usage,and improving the training efficiency.Finally,a dynamic template update and selection method is proposed to fully explore and utilize temporal information,thereby improving the model's performance.ETMTrack achieves state-of-the-art performance on three public datasets,and performs excellently in dealing with challenges such as occlusion and similar appearances,demonstrating the effectiveness and robustness of the tracking algorithm based on temporal modeling.关键词
RGB-T跟踪/时序建模/适配器调优/TransformerKey words
RGB-T tracking/temporal modeling/adapter-tuning/Transformer分类
信息技术与安全科学引用本文复制引用
倪新宇,曾碧,张仲轩..基于高效时序建模的RGB-T跟踪[J].广东工业大学学报,2026,43(2):21-29,9.基金项目
国家自然科学基金资助项目(62172111) （62172111）
广东省自然科学基金资助项目(2021A1515012233) （2021A1515012233）
