华中科技大学学报（自然科学版）2025，Vol.53Issue(3)：41-47,158,8.DOI:10.13245/j.hust.250458
基于Transformer的轻量级单目标跟踪算法
Lightweight single-object tracking algorithm based on Transformer
摘要
Abstract
Lightweight single-object tracking algorithm based on Transformer was proposed to address the problems of low accuracy performance of target tracking algorithms in complex contexts.The algorithm adopted online time-series adaptive convolution to extract local features of the target,and at the same time introduced a lightweight global context module to extract global features,which jointly constructed an efficient target model.In order to cope with the problem of loss of target information in the similarity graph refinement,a lightweight feature augmentation module was constructed,which enhanced the model's ability to express the target without increasing the number of parameters of the overall network,so as to increase the accuracy of the model.Finally,the algorithm added a bounding box refinement module,which can better retain the boundary and scale information of the target and improve the tracking accuracy.The experimental results show that compared with the benchmark algorithm,the tracking accuracy and success rate of this paper's method are improved by 2.5%and 6.9%on the UAV123 dataset,8.8%and 5.9%on the LaSOT dataset,and 2.4%and 2.4%on the OTB100 dataset,respectively.关键词
目标跟踪/Transformer/轻量级/特征增强/边界框细化Key words
object tracking/Transformer/lightweight/feature enhancement/bounding box refinement分类
计算机与自动化引用本文复制引用
黄丹丹,张钰晨,陈广秋,刘智..基于Transformer的轻量级单目标跟踪算法[J].华中科技大学学报（自然科学版）,2025,53(3):41-47,158,8.基金项目
吉林省科技厅重点研发项目(20230201071GX). （20230201071GX）
