Person Re-Identification Based on Text-Assisted Image Information

CHEN Yi; WU Xiao-hong; CHEN Hong-gang; HE Xiao-hai

doi:10.3969/j.issn.2096-6091.2023.18.004

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Person Re-Identification Based on Text-Assisted Image Information

RESEARCH PAPER | 更新时间：2025-03-26

- Person Re-Identification Based on Text-Assisted Image Information
- New Generation of Information Technology Vol. 6, Issue 18, Pages: 20-25(2023)
- 作者机构：
  
  四川大学电子信息学院，四川成都 610065
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.2096-6091.2023.18.004
  CLC： TP391.41
- Published：30 September 2023
- 稿件说明：
移动端阅览
陈怡, 吴晓红, 陈洪刚, 等. 文本辅助图像信息的行人重识别方法[J]. 新一代信息技术, 2023, 6(18): 20-25

CHEN Yi, WU Xiao-hong, CHEN Hong-gang, et al. Person Re-Identification Based on Text-Assisted Image Information[J]. New Generation of Information Technology, 2023, 6(18): 20-25
陈怡, 吴晓红, 陈洪刚, 等. 文本辅助图像信息的行人重识别方法[J]. 新一代信息技术, 2023, 6(18): 20-25 DOI： 10.3969/j.issn.2096-6091.2023.18.004.

CHEN Yi, WU Xiao-hong, CHEN Hong-gang, et al. Person Re-Identification Based on Text-Assisted Image Information[J]. New Generation of Information Technology, 2023, 6(18): 20-25 DOI： 10.3969/j.issn.2096-6091.2023.18.004.

摘要

行人重识别技术（person Re-Identification，Re-ID）旨在从多个不同的摄像头中检索与查询图像相同身份的行人图像。在行人重识别中，不同的行人可能具有相似的外观，而且受到摄像机视角、行人姿势等因素的影响，行人重识别可能存在着较为严重的遮挡问题。为了解决这些问题，我们提出了文本辅助图像信息的行人重识别算法，构建图像通道和文本通道，分别使用OSNet（Omni-Scale Network）网络和SRU（Simple Recurrent Units）网络来提取图像特征和文本特征，其中图像特征分为全局特征和局部特征。文本信息可以准确地描述一个人，对遮挡问题有较高的鲁棒性，全局特征关注行人的整体信息，局部特征提供细粒度的细节信息，因此用文本信息辅以图像信息，可以提高网络模型的鲁棒性。在Market1501和CUHK03两个数据集上进行的实验可以证明本方法的有效性。

Abstract

The person re-identification (Re-ID) technology aims to retrieve pedestrian images with the same identity as the query image from multiple different cameras. In Re-ID

different pedestrians may have similar appearance. And affected by camera viewpoints

pedestrian pose and other factors

person re-identification may face serious occlusion problems. In order to solve these problems

we propose a Re-ID algorithm for text assisted image information

construct image channels and text channels

and use OSNet (Omni-Scale Network) network and SRU (Simple Recurrent Units) network to extract image features and text features

respectively. Image features are divided into global features and local features. Text information can accurately describe a person

and has high robustness to occlusion problems. Global features focus on the overall information of pedestrians

and local features provide fine-grained details. Therefore

image information supplemented with text information can improve the robustness of the network model. Experiments on Market1501 and CUHK03 datasets can prove the effectiveness of this method.

关键词

Keywords

references

GHEISSARI N , SEBASTIAN T B , HARTLEY R . Person reidentification using spatiotemporal appearance [C ] // 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR’06) . Piscataway : IEEE , 2006 , 2 : 1528 - 1535 .

李擎 , 胡伟阳 , 李江昀 , 等 . 基于深度学习的行人重识别方法综述 [J ] . 工程科学学报 , 2022 , 44 ( 5 ): 920 - 932 .

LI Q , HU W Y , LI J Y , et al . A survey of person re-identification based on deep learning [J ] . Chinese Journal of Engineering , 2022 , 44 ( 5 ): 920 - 932 . (in Chinese)

LUO H , JIANG W , GU Y Z , et al . A strong baseline and batch normalization neck for deep person re-identification [J ] . IEEE Transactions on Multimedia , 2019 , 22 ( 10 ): 2597 - 2609 .

SUN Y , ZHENG L , YANG Y , et al . Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline) [C ] // ECCV 2018—Proceedings of the 15th European Conference on Computer Vision . New York : ACM , 2018 : 480 - 496 .

ZHENG W S , LI X , XIANG T , et al . Partial person re-identification [C ] // 2015 IEEE International Conference on Computer Vision (ICCV) . Piscataway : IEEE , 2015 : 4678 - 4686 .

WANG W M , CHEN Y B , WANG D W , et al . Joint attribute soft-sharing and contextual local: a multi-level features learning network for person re-identification [J/OL ] . The Visual Computer , 2023 : 1 - 14 . https://link.springer.com/article/10.1007/s00371-023-02914-x https://link.springer.com/article/10.1007/s00371-023-02914-x .

QIAN X , FU Y , XIANG T , et al . Pose-normalized image generation for person re-identification [C ] // Computer Vision – ECCV 2018 . Cham : Springer International Publishing , 2018 : 661 - 678 .

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

PERVAIZ N , FRAZ M M , SHAHZAD M . Per-former: Rethinking person re-identification using transformer augmented with self-attention and contextual mapping [J ] . The Visual Computer , 2023 , 39 ( 9 ): 4087 - 4102 .

LIU H , FENG J S , QI M B , et al . End-to-end comparative attention networks for person re-identification [J ] . IEEE transactions on image processing , 2017 , 26 ( 7 ): 3492 - 3506 .

YANG F , YAN K , LU S J , et al . Attention driven person re-identification [J ] . Pattern Recognition , 2019 , 86 : 143 - 155 .

YU Y B , YANG S R , HU H F , et al . Attention-guided multi-clue mining network for person re-identification [J ] . Neural Processing Letters , 2022 , 54 ( 4 ): 3201 - 3214 .

CHENG D , GONG Y H , ZHOU S P , et al . Person re-identification by multi-channel parts-based CNN with improved triplet loss function [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2016 : 1335 - 1344 .

HERMANS A , BEYER L , LEIBE B . In defense of the triplet loss for person re-identification [EB/OL ] . ( 2017-11-21 )[ 2024 - 0208 ] . https://arxiv.org/abs/1703.07737 https://arxiv.org/abs/1703.07737 .

VARIOR R R , SHUAI B , LU J W , et al . A siamese long short-term memory architecture for human re-identification [C ] // Computer Vision – ECCV 2016 . Cham : Springer International Publishing , 2016 : 135 - 153 .

WANG Y C , CHEN Z Z , WU F , et al . Person re-identification with cascaded pairwise convolutions [C ] // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 1470 - 1478 .

LI W Q , ZOU C , WANG M , et al . DC-Former: Diverse and Compact Transformer for Person Re-Identification [EB/OL ] . ( 2023-02-28 )[ 2024-02-08 ] . https://arxiv.org/abs/2302.14335 https://arxiv.org/abs/2302.14335 .

ZHOU K Y , YANG Y X , CAVALLARO A , et al . Omni-scale feature learning for person re-identification [C ] // 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway : IEEE , 2019 : 3702 - 3712 .

LEI T , ZHANG Y , WANG S , et al . Simple recurrent units for highly parallelizable recurrence [EB/OL ] . ( 2018-09-07 )[ 2024-02-08 ] . https://arxiv.org/abs/1709.02755 https://arxiv.org/abs/1709.02755 .

HAN C M , JIANG B , TANG J . Multi-granularity cross attention network for person re-identification [J ] . Multimedia Tools and Applications , 2023 , 82 ( 10 ): 14755 - 14773 .

YE M , SHEN J B , LIN G J , et al . Deep learning for person re-identification: A survey and outlook [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022 , 44 ( 6 ): 2872 - 2893 .

TAN H C , LIU X P , BIAN Y H , et al . Incomplete descriptor mining with elastic loss for person re-identification [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2022 , 32 ( 1 ): 160 - 171 .

LIAO K Y , WANG K E , ZHENG Y L , et al . Multi-scale saliency features fusion model for person re-identification [J ] . Multimedia Tools and Applications , 2023 : 1 - 16 .

CHEN X M , ZHENG X T , LU X Q . Bidirectional interaction network for person re-identification [J ] . IEEE Transactions on Image Processing , 2021 , 30 : 1935 - 1948 .

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