TY - JOUR
T1 - Two-stage person re-identification scheme using cross-input neighborhood differences
AU - Kim, Hyeonwoo
AU - Kim, Hyungjoon
AU - Ko, Bumyeon
AU - Shim, Jonghwa
AU - Hwang, Eenjun
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1A4A1031864).
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022/2
Y1 - 2022/2
N2 - Person re-identification aims to identify images of a particular person captured from different cameras or the same camera under different conditions. Person re-identification is conducted using an identification model that classifies the identity of the selected person or a verification model that discriminates between positive and negative image pairs. To further improve the re-identification performance, various methods have combined identification loss with verification loss. However, because such methods compare identities using one-dimensional embedding features without spatial information, local relationships are not considered. Thus, in this paper, we propose a two-stage person re-identification scheme using feature extraction and feature comparison networks. The former generates feature maps with spatial information, and the latter calculates their neighborhood and global differences. We conducted extensive experiments using well-known person re-identification datasets, and the proposed model achieved rank-1 accuracies of 84% and 88.4% for CUHK03 and Market-1501, respectively.
AB - Person re-identification aims to identify images of a particular person captured from different cameras or the same camera under different conditions. Person re-identification is conducted using an identification model that classifies the identity of the selected person or a verification model that discriminates between positive and negative image pairs. To further improve the re-identification performance, various methods have combined identification loss with verification loss. However, because such methods compare identities using one-dimensional embedding features without spatial information, local relationships are not considered. Thus, in this paper, we propose a two-stage person re-identification scheme using feature extraction and feature comparison networks. The former generates feature maps with spatial information, and the latter calculates their neighborhood and global differences. We conducted extensive experiments using well-known person re-identification datasets, and the proposed model achieved rank-1 accuracies of 84% and 88.4% for CUHK03 and Market-1501, respectively.
KW - Convolutional neural networks
KW - Deep learning
KW - Feature representation
KW - Image processing
KW - Person re-identification
UR - http://www.scopus.com/inward/record.url?scp=85111159549&partnerID=8YFLogxK
U2 - 10.1007/s11227-021-03994-z
DO - 10.1007/s11227-021-03994-z
M3 - Article
AN - SCOPUS:85111159549
SN - 0920-8542
VL - 78
SP - 3356
EP - 3373
JO - The Journal of Supercomputing
JF - The Journal of Supercomputing
IS - 3
ER -