计算机工程与应用2025，Vol.61Issue(6)：254-262,9.DOI:10.3778/j.issn.1002-8331.2311-0077
引入特征融合和Transformer模型预测器的目标跟踪算法
Target Tracking Algorithm with Feature Fusion and Transformer Based Model Predictor
摘要
Abstract
Discriminative correlation filters(DCF)have achieved much success in visual tracking.However,most of them simply rely on the features extracted by the last layer of the backbone,while ignoring the low-level rich structural information.In view of this,a target tracking algorithm based on the feature fusion module and the Transformer structure model predictor is proposed.Firstly,a feature fusion module is introduced that integrates the low-level feature and high-level feature via a pyramidal structure.Then,a modified Transformer with asymmetric positional encoding scheme is applied to predict the weights of the model,which can effectively release the expressive ability of the model.Finally,a feature refinement module is employed to optimize the search features.Compared with the existing works,the tracker achieves better feature expression and more precise target localization.Extensive experiments on 3 mainstream datasets,TrackingNet,LaSOT and UAV123,demonstrate that the tracker gains prominent tracking results.关键词
特征融合/Transformer/目标跟踪/特征优化/目标分类Key words
feature fusion/Transformer/object tracking/feature refinement/object classification分类
信息技术与安全科学引用本文复制引用
龚小梅,张轶,胡术..引入特征融合和Transformer模型预测器的目标跟踪算法[J].计算机工程与应用,2025,61(6):254-262,9.基金项目
国家自然科学基金(U20A20161). （U20A20161）
