|Table of Contents|

A Survey of Person Re-identification Based on Deep Learning(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2018年04期
Page:
93-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
A Survey of Person Re-identification Based on Deep Learning
Author(s):
Zhu Fan; Wang Hongyuan; Zhang Ji
School of Information Science and Engineering,Changzhou University,Changzhou 213164,China
Keywords:
deep learning; person re-identification; local feature learning; distance metric learning
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2018.04.015
Abstract:
Due to changes in perspectives,backgrounds,lighting conditions,and mutual occlusion,person re-identification is still a challenging issue. In recent years,many researchers have introduced deep learning methods into person re-identification research and obtained better re-identification results. This paper introduces the main research methods of person re-identification based on deep learning(local feature learning,distance metric learning,video sequence learning,and generation of confrontation networks),and introduces commonly used person re-identification data sets for deep learning(DukeMTMC-reID,CUHK03,and Market1501)and their existing problems. At the same time,it puts forward their own understanding and viewpoints on person re-identification,and finally points out possible future research directions.

References:

[1] BAI X,YANG M K,HUANG T T,et al. Deep-person:learning discriminative deep features for person re-identification[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1711.10658.pdf.
[2]VARIOR R R,SHUAI B,LU J W,et al. A siamese long short-term memory architecture for human re-identification[C]//Proceedings of the European Conference on Computer Vision. Cham:Springer,2016:135-153.
[3]ZHAO H Y,TIAN M Q,SUN S Y,et al. Spindle net:person re-identification with human body region guided feature decomposition and fusion[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii,2017:1077-1085.
[4]WEI L H,ZHANG S L,YAO H T,et al. Glad:global-local-alignment descriptor for pedestrian retrieval[C]//Proceedings of the 2017 ACM on Multimedia Conference. California,2017:420-428.
[5]ZHANG L,XIANG T,GONG S G. Learning a discriminative null space for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,2016:1239-1248.
[6]ZHENG L,YANG Y,HAUPTMANN A G. Person re-identification:past,present and future[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1610.02984.pdf.
[7]ZHOU S,WANG J J,WANG J Y,et al. Point to set similarity based deep feature learning for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Hawaii,2017:3741-3750.
[8]HERMANS A,BEYER L,LEIBE B. In defense of the triplet loss for person re-identification[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1703.07737.pdf.
[9]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]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,2016:1335-1344.
[10]CHEN W H,CHEN X T,ZHANG J G,et al. Beyond triplet loss:a deep quadruplet network for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii,2017:403-412.
[11]XIAO Q Q,LUO H,ZHANG C. Margin sample mining loss:a deep learning based method for person re-identification[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1710.00478.pdf.
[12]MCLAUGHLIN N,RINCON J M D,MILLER P. Recurrent convolutional network for video-based person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,2016:1325-1334.
[13]ZHANG D Y,WU W X,CHENG H,et al. Image-to-video person re-identification with temporally memorized similarity learning[J]. IEEE transactions on circuits & systems for video technology,2017,PP(99):1-1.
[14]MCLAUGHLIN N,RINCON J M D,MILLER P. Recurrent convolutional network for video-based person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,2016:1325-1334.
[15]HUANG W J,LIANG C,YU Y,et al. Video-based person re-identification via self paced weighting[C]//Proceedings of the Thirty-Second Conference on Artificial Intelligence. Louisiana,2018:2273-2280.
[16]LIU H,JIE Z Q,JAYASHREE K,et al. Video-based person re-identification with accumulative motion context[J]. IEEE transactions on circuits & systems for video technology,2017,PP(99):1-1.
[17]SONG G L,LENG B,LIU Y,et al. Region-based quality estimation network for large-scale person re-identification[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1711.08766.pdf.
[18]ZHENG Z D,ZHENG L,YANG Y. Unlabeled samples generated by gan improve the person re-identification baseline in vitro[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1701.07717.pdf.
[19]ZHONG Z,ZHENG L,ZHENG Z D,et al. Camera style adaptation for person re-identification[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1711.10295.pdf.
[20]WEI L H,ZHANG S L,GAO W,et al. Person transfer GAN to bridge domain gap for person re-identification[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1711.08565.pdf.
[21]QIAN X L,FU Y W,WANG W,et al. Pose-normalized image generation for person re-identification[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1712.02225.pdf.
[22]HE L X,LIANG J,LI H Q,et al. Deep Spatial feature reconstruction for partial person re-identification:alignment-free approach[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1801.00881.pdf.
[23]SUN Y F,ZHENG L,DENG W J,et al. Svdnet for pedestrian retrieval[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1703.05693.pdf.
[24]ZHONG Z,ZHENG L,CAO D,et al. Re-ranking person re-identification with k-reciprocal encoding[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Hawaii:IEEE,2017:3652-3661.
[25]ZHONG Z,ZHENG L,KANG G L,et al. Random erasing data augmentation[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1708.04896.pdf.
[26]SARFRAZ M S,SCHUMANN A,EBERLE A,et al. A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1711.10378.pdf.
[27]FAN H H,ZHENG L,YANG Y. Unsupervised person re-identification:clustering and fine-tuning[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1705.10444.pdf.
[28]BAK S,CARR P,LALONDE J F,et al. Domain adaptation through synthesis for unsupervised person re-identification[DB/OL]. [2018-10-22]. https://arxiv.org/pdf/1804.10094.pdf.
[29]JOSE C,FLEURET F. Scalable metric learning via weighted approximate rank component analysis[C]//European Conference on Computer Vision. Cham:Springer,2016:875-890.
[30]VARIOR R R,HALOI M,WANG G. Gated siamese convolutional neural network architecture for human re-identification[C]//European Conference on Computer Vision. Cham:Springer,2016:791-808.
[31]XIAO T,LI H S,OUYANG W L,et al. Learning deep feature representations with domain guided dropout for person re-identification[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,2016:1249-1258.
[32]ZHOU Z,HUANG Y,WANG W,et al. See the forest for the trees:joint spatial and temporal recurrent neural networks for video-based person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii,2017:6776-6785.

Memo

Memo:
-
Last Update: 2018-12-30