多方引导前景增强的行人重识别方法

doi:10.3785/j.issn.1008-973X.2025.05.006

浙江大学学报(工学版)

2025, Vol. 59

Issue (5): 929-937 DOI: 10.3785/j.issn.1008-973X.2025.05.006

计算机技术、信息工程

多方引导前景增强的行人重识别方法

刘俊婧1(

),郑宛露1,郭子强1,王少荣1,2,3,*(

)

1. 北京林业大学信息学院，北京 100083
2. 国家林业草原林业智能信息处理工程技术研究中心，北京 100083
3. 北京市虚拟仿真与可视化工程技术研究中心，北京 100871

Person re-identification method based on multi-part guided foreground enhancement

Junjing LIU1(

),Wanlu ZHENG1,Ziqiang GUO1,Shaorong WANG1,2,3,*(

)

1. School of Information Science and Technology, Beijing Forest University, Beijing 100083, China
2. Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
3. Beijing Virtual Simulation and Visualization Engineering Center, Beijing 100871, China

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摘要：

为了解决行人重识别模型性能对背景环境因素过于依赖的问题, 提出多方引导前景增强的行人重识别方法. 该方法通过掩码引导增强和自增强策略，提升了模型对行人前景的关注，同时保留一定的背景信息，有效减轻了对背景信息的依赖，增强了模型的泛化能力. 在骨干网络中引入瓶颈优化模块，利用空洞卷积，在保持原有参数规模的前提下，有效增大了模型的感受野，提升了模型的整体性能. 实验结果表明, 提出的模型在Market1501和DukeMTMC_reID数据集上分别取得了95%和88.3%的Rank-1准确率. 验证了多方引导前景增强的行人重识别方法的有效性，通过前景增强并结合一定的背景信息，有效提升了基线模型的性能.

关键词： 行人重识别; 人体解析; 背景变化; 前景增强; 掩码引导增强

Abstract:

A multi-part guided foreground enhancement method for person re-identification was proposed in order to solve the problem that the performance of person re-identification model was overly depend on background environmental factors. The model’s attention to the person’s foreground was enhanced by employing mask-guided enhancement and self-enhancement strategies, while retaining some background information. This effectively reduced the model’s dependence on background information and improved its generalization ability. A bottleneck optimization module was integrated into the backbone network, utilizing dilated convolutions to enlarge the model’s receptive field while maintaining the original parameter scale, thereby improving the overall performance of the model. The experimental results demonstrated that the proposed model achieved Rank-1 accuracies of 95% and 88.3% on the Market1501 and DukeMTMC_reID datasets, respectively. The effectiveness of the multi-part guided foreground enhancement method was verified, which strengthened the foreground while incorporating appropriate background information, and significantly enhanced the performance of the baseline model.

Key words: person re-identification human parsing background change foreground enhancement mask-guided enhancement

收稿日期: 2024-07-06 出版日期: 2025-04-25

CLC:

TP 391

通讯作者: 王少荣 E-mail: L_JunJing@bjfu.edu.cn;shaorongwang@hotmail.com

作者简介: 刘俊婧（1999—），女，硕士，从事计算机视觉的研究. orcid.org/0000-0001-8800-8194. E-mail：L_JunJing@bjfu.edu.cn

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引用本文:

刘俊婧,郑宛露,郭子强,王少荣. 多方引导前景增强的行人重识别方法[J]. 浙江大学学报(工学版), 2025, 59(5): 929-937.

Junjing LIU,Wanlu ZHENG,Ziqiang GUO,Shaorong WANG. Person re-identification method based on multi-part guided foreground enhancement. Journal of ZheJiang University (Engineering Science), 2025, 59(5): 929-937.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.05.006 或 https://www.zjujournals.com/eng/CN/Y2025/V59/I5/929

图 1 人体解析预测的示例图

图 2 行人图像处理

图 3 强基线网络

表 1 使用Market1501数据集原始图像的训练结果

表 2 使用DukeMTMC数据集原始图像的训练结果

表 3 使用Market1501数据集背景图像的训练结果

表 4 使用DukeMTMC数据集背景图像的训练结果

表 5 使用Market1501数据集前景图像的训练结果

表 6 使用DukeMTMC数据集前景图像的训练结果

图 4 基于多方引导前景增强的行人重识别模型

图 5 瓶颈优化模块

表 7 行人重识别模型的实验硬件与软件环境配置

表 8 行人重识别模型训练参数的配置

表 9 提出方法与现有行人重识别方法的性能对比

表 10 模块组合对模型前景识别性能的影响

表 11 各模块对提升模型识别性能的有效性实验结果

表 12 各模块对提升模型泛化性的有效性实验结果

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