计算机科学与探索2025，Vol.19Issue(10)：2769-2781,13.DOI:10.3778/j.issn.1673-9418.2501038
多元特征提取与通道特征重构的跨模态行人重识别方法
Multivariate Feature Extraction and Channel Feature Reconstruction Cross-Modality Person Re-identification Method
摘要
Abstract
To address the problem of difficult visible-infrared person re-identification matching caused by significant modal differences between visible light images and infrared images,a multivariate feature extraction and channel feature recon-struction cross-modality person re-identification method is proposed.Firstly,the dual-stream ResNeXt50 serves as the backbone network,extracting different sub-network features through channel grouping convolution.This approach mitigates the imbalance in channel numbers between the two modalities,enhances discriminative feature extraction capabilities,and reduces model complexity to prevent overfitting.Secondly,a multi-level feature reconstruction module is designed to recon-struct and fuse features from various stages in the channel dimension.The channel attention mechanism and adaptive weights are employed to emphasize key discriminative features,eliminate redundant information,and boost the identification capacity of model.Finally,a multi-dimensional feature extraction module is constructed to extract shared features across multiple modalities through parallel convolution of multiple branches.The EMA(efficient multi-scale attention module)attention mechanism is utilized to capture details and global information in the image through feature grouping and cross spatial learning methods.Effective spatial and channel features are learnt to enhance the ability of network to learn key pedestrian features in complex scenes.On the SYSU-MM01 dataset in all search mode,the proposed method achieves rank-1 and mAP scores of 75.35%and 72.37%,respectively.In the indoor search mode,the rank-1 and mAP scores reach 83.57%and 86.03%,respectively.On the RegDB dataset in the visible-infrared retrieval mode,the rank-1 and mAP scores are 93.21%and 87.09%,respectively,while in the infrared-visible retrieval mode,the rank-1 and mAP scores are 91.63%and 86.00%,respectively,demonstrating the effectiveness of the method.关键词
可见光-红外图像/行人重识别/注意力机制/特征提取Key words
visible-infrared image/person re-identification/attention mechanism/feature extraction分类
信息技术与安全科学引用本文复制引用
王铭杰,毕艺瀚,王蓉,李冲..多元特征提取与通道特征重构的跨模态行人重识别方法[J].计算机科学与探索,2025,19(10):2769-2781,13.基金项目
国家自然科学基金(62076246) （62076246）
中央高校基本科研业务费专项资金(2024JKF12).This work was supported by the National Natural Science Foundation of China(62076246),and the Fundamental Research Funds for the Central Universities of China(2024JKF12). （2024JKF12）
