基于多粒度融合和跨尺度感知的跨模态行人重识别

程德强; 姬广凯; 张皓翔; 江鹤; 寇旗旗

doi:10.11959/j.issn.1000-436x.2025019

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基于多粒度融合和跨尺度感知的跨模态行人重识别

学术论文 | 更新时间：2025-02-13

- 基于多粒度融合和跨尺度感知的跨模态行人重识别
- Cross-modality person re-identification based on multi-granularity fusion and cross-scale perception
- 通信学报 2025年46卷第1期页码：108-123
- 作者机构：
  
  1.中国矿业大学信息与控制工程学院，江苏徐州 221116
  2.中国矿业大学计算机科学与技术学院，江苏徐州 221116
- 作者简介：
  
  [ "程德强（1979- ），男，河南洛阳人，博士，中国矿业大学教授、博士生导师，主要研究方向为机器视觉与模式识别、图像智能检测与信息处理等。" ]
  [ "姬广凯（1999- ），男，山东泰安人，中国矿业大学硕士生，主要研究方向为跨模态行人重识别。" ]
  [ "张皓翔（1994- ），男，江苏徐州人，中国矿业大学博士生，主要研究方向为目标检测、图像检索等。" ]
  [ "江鹤（1990- ），男，江苏徐州人，博士，中国矿业大学讲师，主要研究方向为图像增强与修复、图像检测、图像识别等。" ]
  [ "寇旗旗（1988- ），男，河南襄城人，博士，中国矿业大学副教授，主要研究方向为图像增强与复原、智能检测与模式识别。" ]
- 基金信息：
  
  国家自然科学基金资助项目(52204117;52304182);济宁市重点研发计划基金资助项目(2023KJHZ007)
- DOI：10.11959/j.issn.1000-436x.2025019
  中图分类号： TP391
- 收稿日期：2024-09-02，
  
  修回日期：2024-12-11，
  
  纸质出版日期：2025-01-25
- 稿件说明：
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程德强,姬广凯,张皓翔等.基于多粒度融合和跨尺度感知的跨模态行人重识别[J].通信学报,2025,46(01):108-123.

CHENG Deqiang,JI Guangkai,ZHANG Haoxiang,et al.Cross-modality person re-identification based on multi-granularity fusion and cross-scale perception[J].Journal on Communications,2025,46(01):108-123.
程德强,姬广凯,张皓翔等.基于多粒度融合和跨尺度感知的跨模态行人重识别[J].通信学报,2025,46(01):108-123. DOI： 10.11959/j.issn.1000-436x.2025019.

CHENG Deqiang,JI Guangkai,ZHANG Haoxiang,et al.Cross-modality person re-identification based on multi-granularity fusion and cross-scale perception[J].Journal on Communications,2025,46(01):108-123. DOI： 10.11959/j.issn.1000-436x.2025019.

摘要

提出一种基于多粒度融合和跨尺度感知的跨模态行人重识别网络，该网络能够有效提取行人图像特征并减少图像间的模态差异。首先，提出多尺度特征融合注意力机制并设计一种多粒度非局部融合框架，有效融合不同模态和不同尺度的图像特征；其次，提出一种跨尺度特征信息感知策略，该策略可有效降低因视角变化、行人背景变化等产生的无关噪声对行人判别的影响；最后，针对行人图像特征信息不足，设计并行空洞卷积残差模块，获取更为丰富的行人特征信息。将所提方法在2个标准公共数据集与当前先进的跨模态行人重识别方法比较。实验结果表明，所提方法在SYSU-MM01数据集的全搜索模式下的R-1和平均精度（mAP）分别达到75.9%和73.3%，在RegDB数据集的可见光到红外的搜索（VIS to IR）模式下的Rank-1和mAP分别达到93.7%和89.3%，优于所对比的方法，充分证实了所提方法的有效性。

Abstract

A cross-modality person re-identification network based on multi-granularity fusion and cross-scale perception was proposed

which could effectively extract person image features and reduce the modality discrepancies between images. Firstly

a multi-scale feature fusion attention mechanism was proposed

and a multi-granularity non-local fusion framework was designed to effectively integrate image features from different modalities and scales. Secondly

a cross-scale feature information perception strategy was proposed

which could effectively reduce the influence of irrelevant noise caused by the change of perspective and person background on person discrimination. Finally

in view of the lack of person image feature information

a parallel dilated convolution residual module was designed to obtain more abundant person feature information. The proposed method was compared with current state-of-the-art cross-modal person re-identification algorithms on two standard public datasets. Experimental results show that the Rank-1 and mAP of the proposed method reach 75.9% and 73.3%

respectively

in the all search mode of the SYSU-MM01 dataset

and 93.7% and 89.3% in the VIS to IR retrieval mode of the RegDB dataset

respectively

which is better than the compared methods

which fully confirms the effectiveness of the proposed method.

关键词

Keywords

references

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