软件导刊2025，Vol.24Issue(3)：200-205,6.DOI:10.11907/rjdk.241086
基于Res2Net-Transformer的多尺度特征融合行人重识别算法
Person Re-identification Algorithm Based on Res2Net-Transformer for Multi-Scale Feature Fusion
葛娟娟1
作者信息
- 1. 桂林理工大学 计算机科学与工程学院,广西 桂林 541006
- 折叠
摘要
Abstract
To accurately recognize pedestrian images,a person re-identification algorithm based on Res2Net-Transformer for multi-scale feature fusion is proposed.This method consists of a global feature extraction module,a deep aggregation module,and a feature alignment module.In the global feature extraction module,the Res2Net module is introduced into the ResNet50 network,enabling the network to extract more fine-grained features.The multi-scale deep aggregation module achieves the recursive aggregation of multi-scale features.The feature alignment module is used to mitigate the recognition impact caused by feature misalignment.Comparison with existing methods,this method approach demonstrates better robustness on Market1501、DukeMTMC-reID and MSMT17 dataset,it yields superior results in pedestrian re-identification.关键词
行人重识别/图像识别/特征对齐/多尺度特征融合Key words
person re-identification/image recognition/feature alignment/multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
葛娟娟..基于Res2Net-Transformer的多尺度特征融合行人重识别算法[J].软件导刊,2025,24(3):200-205,6.
