电子科技大学学报2025，Vol.54Issue(3)：393-400,8.DOI:10.12178/1001-0548.2024091
融合注意力机制的多粒度行人再识别方法
Multi-granularity person re-identification with attention mechanism fusion
摘要
Abstract
A multi-granularity person re-identification model integrating attention mechanism is proposed to address the complex monitoring environment and the significant differences in image appearance of pedestrians under different conditions such as lighting changes,perspective changes,and occlusion,which make it difficult to extract detailed pedestrian features.The model extracts feature maps containing information at different scales by introducing a multi-branch structure.Combining multi-granularity segmentation modules and the attention mechanism,the local discriminative information of the feature map is further extracted to obtain diverse feature representations and achieve the coordination and unity of features.The model is supervised and trained by using federated learning to obtain a more comprehensive feature description.Excellent performance has been achieved on the mainstream person re-identification datasets Market-1501,DukeMTMC-reID,and CUHK03,with mAP reaching 88.42%,78.86%,and 76.70%,respectively,demonstrating the effectiveness of the proposed model.关键词
行人再识别/多尺度多分支/多粒度特征/注意力机制/特征融合Key words
person re-identification/multi scale and multi branch/multi granularity features/attention mechanism/feature fusion分类
计算机与自动化引用本文复制引用
莫太平,覃汉岳,孙鹏,张向文,孟春城..融合注意力机制的多粒度行人再识别方法[J].电子科技大学学报,2025,54(3):393-400,8.基金项目
国家自然科学基金(62263006) （62263006）
广西自动检测技术与仪器重点实验室基金(YQ21107) （YQ21107）
