Person re-identification with intra-domain similarity grouping based on semantic fusion

Qiqi KOU; Ji HUANG; Deqiang CHENG; Yunlong LI; Jianying ZHANG

doi:10.11959/j.issn.1000-436x.2022136

您当前的位置：

首页 >

文章列表页 >

Person re-identification with intra-domain similarity grouping based on semantic fusion

Papers | 更新时间：2024-06-05

- Person re-identification with intra-domain similarity grouping based on semantic fusion
- Journal on Communications Vol. 43, Issue 7, Pages: 153-162(2022)
- 作者机构：
  
  1. 中国矿业大学计算机科学与技术学院，江苏徐州 221116
  2. 中国矿业大学信息与控制工程学院，江苏徐州 221116
- 作者简介：
- 基金信息：
  
  The Fundamental Research Funds for the Central Universities(2020QN49)
- DOI：10.11959/j.issn.1000-436x.2022136
  CLC： TP391
- Online First：2022-06，
  
  Published：25 July 2022
- 稿件说明：
移动端阅览
Qiqi KOU, Ji HUANG, Deqiang CHENG, et al. Person re-identification with intra-domain similarity grouping based on semantic fusion[J]. Journal on Communications, 2022, 43(7): 153-162.
DOI：

Qiqi KOU, Ji HUANG, Deqiang CHENG, et al. Person re-identification with intra-domain similarity grouping based on semantic fusion[J]. Journal on Communications, 2022, 43(7): 153-162. DOI： 10.11959/j.issn.1000-436x.2022136.

摘要

无监督跨域行人重识别旨在使有标签源域数据集上训练的模型适应目标域数据集。然而，基于聚类的无监督跨域行人重识别算法在网络特征学习过程中常因输入行人图片情况各异而产生噪声，从而影响聚类效果。针对这一问题，提出一种基于语义融合的域内相似性分组行人重识别网络，首先在Baseline网络的基础上添加语义融合层，依次从空间和通道2个方面对中间特征图进行相似特征的语义融合，从而提升网络的自适应感知能力。此外，通过充分利用域内相似性细粒度信息，进而提高网络对全局和局部特征的聚类精准度。通过在DukeMTMC-ReID、Market1501和MSMT17这3个公开数据集上进行实验，结果表明，所提算法的均值平均精度（mAP）和Rank识别准确率与近年无监督跨域行人重识别算法相比有显著提升。

Abstract

Unsupervised cross-domain person re-identification aims to adapt a model trained on a labeled source-domain dataset to a target-domain dataset.However

the cluster-based unsupervised cross-domain pedestrian re-identification algorithm often generates noise due to the different input pedestrian pictures during the network feature learning process

which affects the clustering results.To solve this problem

An intra-domain similarity grouping pedestrian re-identification network based on semantic fusion was proposed.Firstly

a semantic fusion layer was added on the basis of the Baseline network

and the semantic fusion of similar features was performed on the intermediate feature maps from the two aspects of space and channel in turn

so as to improve the adaptive perception ability of the network.In addition

by making full use of the fine-grained information of intra-domain similarity

the network’s clustering accuracy of global and local features was improved.Experiments were carried out on three public datasets

DukeMTMC-ReID

Market1501

MSMT17

and the results demonstrate that the mAP and Rank recognition accuracy are significantly improved compared with recent unsupervised cross-domain person re-identification algorithms.

关键词

Keywords

references

LI J H , CHENG D Q , LIU R H , et al . Unsupervised person re-identification based on measurement axis [J ] . IEEE Signal Processing Letters , 2021 , 28 : 379 - 383 .

ZHAO K , CHENG D Q , KOU Q Q , et al . Sequences consistency feature learning for video-based person re-identification [J ] . Electronics Letters , 2022 , 58 ( 4 ): 142 - 144 .

任雪娜 , 张冬明 , 包秀国 , 等 . 语义引导的遮挡行人再识别注意力网络 [J ] . 通信学报 , 2021 , 42 ( 10 ): 106 - 116 .

REN X N , ZHANG D M , BAO X G , et al . Semantic guidance atten-tion network for occluded person re-identification [J ] . Journal on Communications , 2021 , 42 ( 10 ): 106 - 116 .

SONG L C , WANG C , ZHANG L F , et al . Unsupervised domain adaptive re-identification:theory and practice [J ] . Pattern Recognition , 2020 ,102:107173.

QI L , WANG L , HUO J , et al . A novel unsupervised camera-aware domain adaptation framework for person re-identification [C ] // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2019 : 8079 - 8088 .

CHEN Y B , ZHU X T , GONG S G . Instance-guided context rendering for cross-domain person re-identification [C ] // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2019 : 232 - 242 .

LI Y J , LIN C S , LIN Y B , et al . Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation [C ] // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2019 : 7918 - 7928 .

LIN X T , REN P Z , YEH C H , et al . Unsupervised person re-identification:a systematic survey of challenges and solutions [J ] . arXiv Preprint,arXiv:2109.06057 , 2021 .

GOODFELLOW I , POUGET-ABADIE J , MIRZA M , et al . Generative adversarial networks [J ] . Communications of the ACM , 2020 , 63 ( 11 ): 139 - 144 .

ZHONG Z , ZHENG L , LUO Z M , et al . Invariance matters:exemplar memory for domain adaptive person re-identification [C ] // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2019 : 598 - 607 .

MEKHAZNI D , BHUIYAN A , EKLADIOUS G , et al . Unsupervised domain adaptation in the dissimilarity space for person re-identifica tion [C ] // Computer Vision - ECCV 2020 . Cham:Springer International Publishing , 2020 : 159 - 174 .

YANG Q Z , YU H X , WU A C , et al . Patch-based discriminative feature learning for unsupervised person re-identification [C ] // Pro ceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2019 : 3628 - 3637 .

YU H X , ZHENG W S , WU A C , et al . Unsupervised person re-identification by soft multilabel learning [C ] // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2019 : 2143 - 2152 .

JIANG K , ZHANG T , ZHANG Y , et al . Self-supervised agent learning for unsupervised cross-domain person re-identification [J ] . IEEE Transactions on Image Processing:a Publication of the IEEE Signal Processing Society , 2020 , 29 : 8549 - 8560 .

ZHAO R , OUYANG W L , WANG X G . Unsupervised salience learning for person re-identification [C ] // Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2013 : 3586 - 3593 .

RUBLEE E , RABAUD V , KONOLIGE K , et al . ORB:an efficient alternative to SIFT or SURF [C ] // Proceedings of 2011 International Conference on Computer Vision . Piscataway:IEEE Press , 2011 : 2564 - 2571 .

JADERBERG M , SIMONYAN K , ZISSERMAN A , et al . Spatial transformer networks [C ] // Proceedings of the 28th International Conference on Neural Information Processing . Cambridge:MIT Press , 2015 : 2017 - 2025 .

DAI J F , QI H Z , XIONG Y W , et al . Deformable convolutional networks [C ] // Proceedings of 2017 IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2017 : 764 - 773 .

DENG J , DONG W , SOCHER R , et al . ImageNet:a large-scale hierarchical image database [C ] // Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2009 : 248 - 255 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 770 - 778 .

DU Y , YUAN C F , LI B , et al . Interaction-aware spatio-temporal pyramid attention networks for action classification [C ] // Computer Vision - ECCV 2018 . Cham:Springer International Publishing , 2018 : 388 - 404 .

ZHANG S S , YANG J , SCHIELE B . Occluded pedestrian detection through guided attention in CNNs [C ] // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 6995 - 7003 .

WANG G S , YUAN Y F , CHEN X , et al . Learning discriminative features with multiple granularities for person re-identification [C ] // Proceedings of the 26th ACM International Conference On Multimedia . New York:ACM Press , 2018 : 274 - 282 .

ESTER M , KRIEGEL H P , SANDER J , et al . A density-based algo rithm for discovering clusters in large spatial databases with noise [C ] // Proceedings of the Second International Conference on Knowledge Discovery and Data Mining . Palo Alto:AAAI Press , 1996 : 226 - 231 .

ZHENG L , SHEN L Y , TIAN L , et al . Scalable person re-identification:a benchmark [C ] // Proceedings of 2015 IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2015 : 1116 - 1124 .

ZHENG Z D , ZHENG L , YANG Y . Unlabeled samples generated by GAN improve the person re-identification baseline in vitro [C ] // Proceedings of 2017 IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2017 : 3774 - 3782 .

WEI L H , ZHANG S L , GAO W , et al . Person transfer GAN to bridge domain GAP for person re-identification [C ] // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 79 - 88 .

LI Y J , YANG F E , LIU Y C , et al . Adaptation and re-identification network:an unsupervised deep transfer learning approach to person re-identification [C ] // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . Piscataway:IEEE Press , 2018 : 285 - 2856 .

WU J L , LIAO S C , LEI Z , et al . Clustering and dynamic sampling based unsupervised domain adaptation for person re-identification [C ] // Proceedings of 2019 IEEE International Conference on Multimedia and Expo . Piscataway:IEEE Press , 2019 : 886 - 891 .

ZHAO Y R , LU H T . Neighbor similarity and soft-label adaptation for unsupervised cross-dataset person re-identification [J ] . Neurocomputing , 2020 , 388 : 246 - 254 .

GE Y , LIU L , ZHANG H X . A three-stage learning approach to cross-domain person re-identification [J ] . Applied Soft Computing , 2021 ,112:107793.

ZHONG Z , ZHENG L , ZHENG Z D , et al . Camera style adaptation for person re-identification [C ] // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 5157 - 5166 .

KINGMA D P , BA J . Adam:a method for stochastic optimization [J ] . arXiv Preprint,arXiv:1412.6980 , 2014 .

WANG D K , ZHANG S L . Unsupervised person re-identification via multi-label classification [C ] // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2020 : 10978 - 10987 .

ZHONG Z , ZHENG L , LUO Z M , et al . Learning to adapt invariance in memory for person re-identification [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021 , 43 ( 8 ): 2723 - 2738 .

SELVARAJU R R , COGSWELL M , DAS A , et al . Grad-CAM:visual explanations from deep networks via gradient-based localization [J ] . International Journal of Computer Vision , 2020 , 128 ( 2 ): 336 - 359 .

Views

1110

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Cross-modality person re-identification based on multi-granularity fusion and cross-scale perception

Related Author

CHENG Deqiang

JI Guangkai

ZHANG Haoxiang

JIANG He

KOU Qiqi

Related Institution

No data

AI问答

⁰