计算机工程2025，Vol.51Issue(12)：96-108,13.DOI:10.19678/j.issn.1000-3428.0069871
基于双维度特征增强的Transformer跟踪器
Transformer Tracker Based on Dual-Dimensional Feature Enhancement
摘要
Abstract
The Siamese tracking network is a popular target tracking framework that includes three modules:backbone,fusion,and positioning networks.The Transformer is a relatively new and effective implementation method for fusion network modules.The encoder and decoder of the Transformer use a self-attention mechanism to enhance the features of the Convolutional Neural Network(CNN).However,the self-attention mechanism can only enhance features in the spatial dimension without considering feature enhancement in the channel dimension.To enable the self-attention network of the Transformer to enhance features both in the spatial and channel dimensions and provide accurate correlation information for the target localization network,a Transformer tracker based on dual-dimensional feature enhancement is proposed to improve the Transformer fusion network.First,using the third-and fourth-stage features of the backbone network as inputs,channel dimension feature enhancement is performed via CAE-Net in the self-attention module of the Transformer encoder and decoder to enhance the importance of the channel.Subsequently,two-stage feature-weighted fusion and linear transformation are performed via SAE-Net to obtain the self-attention factors Q,K,and V.Finally,spatial dimension feature enhancement is performed via a self-attention operation.Experiments conducted on five widely used public benchmark datasets reveal that the improved Transformer feature fusion module can improve the tracking performance of the tracker with minimal reduction in speed of tracking.关键词
目标跟踪/孪生跟踪网络/注意力机制/Transformer/双维度特征增强Key words
object tracking/Siamese tracking network/attention mechanism/Transformer/dual-dimensional feature enhancement分类
信息技术与安全科学引用本文复制引用
YUAN Yinghua,JIN Yingran,GAO Yun..基于双维度特征增强的Transformer跟踪器[J].计算机工程,2025,51(12):96-108,13.基金项目
国家自然科学基金(61802337). （61802337）
