基于多池化融合与背景消除网络的跨数据集行人再识别方法

李艳凤; 张斌; 孙嘉; 陈后金; 朱锦雷

doi:10.11959/j.issn.1000-436x.2020181

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基于多池化融合与背景消除网络的跨数据集行人再识别方法

学术论文 | 更新时间：2024-06-05

- 基于多池化融合与背景消除网络的跨数据集行人再识别方法
- Cross-dataset person re-identification method based on multi-pool fusion and background elimination network
- 通信学报 2020年41卷第10期页码：70-79
- 作者机构：
  
  北京交通大学电子信息工程学院，北京 100093
- 作者简介：
  
  [ "李艳凤（1988- ），女，河北廊坊人，博士，北京交通大学副教授、博士生导师，主要研究方向为图像处理与模式识别" ]
  [ "张斌（1995- ），男，山东德州人，北京交通大学硕士生，主要研究方向为图像处理与模式识别" ]
  [ "孙嘉（1995- ），女，辽宁丹东人，北京交通大学博士生，主要研究方向为图像处理与模式识别" ]
  [ "陈后金（1965- ），男，安徽马鞍山人，博士，北京交通大学教授、博士生导师，主要研究方向为图像处理与模式识别" ]
  [ "朱锦雷（1983- ），男，山东曹县人，北京交通大学博士生，主要研究方向为图像处理与模式识别" ]
- 基金信息：
  
  国家自然科学基金资助项目(61872030);山东省重大科技创新工程基金资助项目(2019TSLH0206)
- DOI：10.11959/j.issn.1000-436x.2020181
  中图分类号： TP391
- 网络出版日期：2020-10，
  
  纸质出版日期：2020-10-25
- 稿件说明：
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李艳凤, 张斌, 孙嘉, 等. 基于多池化融合与背景消除网络的跨数据集行人再识别方法[J]. 通信学报, 2020,41(10):70-79.

Yanfeng LI, Bin ZHANG, Jia SUN, et al. Cross-dataset person re-identification method based on multi-pool fusion and background elimination network[J]. Journal on communications, 2020, 41(10): 70-79.
李艳凤, 张斌, 孙嘉, 等. 基于多池化融合与背景消除网络的跨数据集行人再识别方法[J]. 通信学报, 2020,41(10):70-79. DOI： 10.11959/j.issn.1000-436x.2020181.

Yanfeng LI, Bin ZHANG, Jia SUN, et al. Cross-dataset person re-identification method based on multi-pool fusion and background elimination network[J]. Journal on communications, 2020, 41(10): 70-79. DOI： 10.11959/j.issn.1000-436x.2020181.

摘要

现有跨数据集行人再识别方法一般致力于减小2个数据集之间的数据分布差异，忽略了背景信息对识别性能的影响。针对上述问题，提出了一种基于多池化融合与背景消除网络的跨数据集行人再识别方法。为了兼顾全局特征和局部特征，同时实现特征的多细粒度表示，构建了多池化融合网络。为了使监督网络能提取有用的行人前景特征，构建了特征级有监督背景消除网络。采用结合行人分类损失及特征激活损失的多任务学习损失函数，在3个公开行人再识别数据集上对方法进行评估，当MSMT17作为训练集时，Market-1501上的跨数据集识别性能mAP为35.53%，相比ResNet50网络提升了9.24%；DukeMTMC-reID上的跨数据集识别性能mAP为41.45%，相比于ResNet50网络提升了10.72%。与现有方法相比，所提方法具有更优的跨数据集行人再识别性能。

Abstract

The existing cross-dataset person re-identification methods were generally aimed at reducing the difference of data distribution between two datasets

which ignored the influence of background information on recognition performance.In order to solve this problem

a cross-dataset person re-ID method based on multi-pool fusion and background elimination network was proposed.To describe both global and local features and implement multiple fine-grained representations

a multi-pool fusion network was constructed.To supervise the network to extract useful foreground features

a feature-level supervised background elimination network was constructed.The final network loss function was defined as a multi-task loss

which combined both person classification loss and feature activation loss.Three person re-ID benchmarks were employed to evaluate the proposed method.Using MSMT17 as the training set

the cross-dataset mAP for Market-1501 was 35.53%

which was 9.24% higher than ResNet50.Using MSMT17 as the training set

the cross-dataset mAP for DukeMTMC-reID was 41.45%

which was 10.72% higher than ResNet50.Compared with existing methods

the proposed method shows better cross-dataset person re-ID performance.

关键词

Keywords

references

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