GAN网络混合编码的行人再识别

杨琦; 车进; 张良; 张玉霞

doi:10.37188/CJLCD.2020-0167

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GAN网络混合编码的行人再识别

图像处理 | 更新时间：2021-02-07

- GAN网络混合编码的行人再识别
- Person re-identification of GAN network hybrid coding
- 液晶与显示 2021年36卷第2期页码：334-342
- 作者机构：
  
  1.宁夏大学物理与电子电气工程学院, 宁夏银川 750021
  2.宁夏沙漠信息智能感知重点实验室, 宁夏银川 750021
- 作者简介：
  
  [ "杨琦(1994-), 男, 陕西榆林人, 硕士研究生, 2017年于天津城建大学获得学士学位, 主要从事图像处理、智能视频方面的研究。E-mail:qyang1028@qq.com" ]
  [ "车进(1973-), 男, 宁夏银川人, 博士, 教授, 2014年于天津大学获得博士学位, 主要从事图像处理、智能视频方面的研究。E-mail:koalache@126.com" ]
- 基金信息：
  
  国家自然科学基金(61861037)
- DOI：10.37188/CJLCD.2020-0167
  中图分类号： TP392
- 收稿日期：2020-06-23，
  
  修回日期：2020-09-04，
  
  录用日期：2020-9-4，
  
  纸质出版日期：2021-02
- 稿件说明：
移动端阅览
杨琦, 车进, 张良, 等. GAN网络混合编码的行人再识别[J]. 液晶与显示, 2021,36(2):334-342.

Qi YANG, Jin CHE, Liang ZHANG, et al. Person re-identification of GAN network hybrid coding[J]. Chinese journal of liquid crystals and displays, 2021, 36(2): 334-342.
杨琦, 车进, 张良, 等. GAN网络混合编码的行人再识别[J]. 液晶与显示, 2021,36(2):334-342. DOI： 10.37188/CJLCD.2020-0167.

Qi YANG, Jin CHE, Liang ZHANG, et al. Person re-identification of GAN network hybrid coding[J]. Chinese journal of liquid crystals and displays, 2021, 36(2): 334-342. DOI： 10.37188/CJLCD.2020-0167.

摘要

由于摄像机视角造成的类内差异明显，研究学者开始利用GAN扩充数据保持类内不变性。针对现有GAN生成图像模糊、背景不真实，本文提出一种利用姿态与外观特征混合编码的行人再识别算法。在训练阶段，将人物图像分解为姿态特征和外观特征，生成网络通过切换外观特征与姿态特征，混合两幅图像中的特征生成高质量图像。判别网络将生成图像的外观特征反馈给生成网络的外观编码器以实现联合优化，采用多损失联合进一步提高生成图片的质量。在测试阶段，使用原数据集对网络模型进行测试，在Market-1501和DukeMTMC-reID数据集上的rank-1/mAP分别达到93.4%/82.2%、84.3%/70.5%。

Abstract

Due to the obvious intra-class differences caused by camera perspective

many researchers begin to use GAN to expand data to maintain intra-class invariance. Nevertheless

the images generated by existing GAN have some defects such as blurred image and unreal background. To solve the above existing problems

a person re-identification algorithm that uses mixed coding of posture and appearance features is proposed in this paper. In the training phase

the image of the person is decomposed into posture features and appearance features

and the generated network can generate high-quality images by switching the appearance feature and posture feature and then mixing the features in the two images. The discriminant network feeds back the appearance characteristics of the generated image to the appearance coder of the generated network to achieve joint optimization

and uses multi-loss joint to further improve the quality of the generated image. In the testing phase

the network model is tested using the original data set. The rank-1/mAP on the Market-1501 and DukeMTMC-reID data sets can reach 93.4%/82.2% and 84.3%/70.5% respectively.

关键词

Keywords

references

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