TY - JOUR
T1 - Multi-level and multi-scale horizontal pooling network for person re-identification
AU - Zhang, Yunzhou
AU - Liu, Shuangwei
AU - Qi, Lin
AU - Coleman, Sonya
AU - Kerr, Dermot
AU - Shi, Weidong
N1 - Funding Information:
This work is supported by National Natural Science Foundation of China (No. 61973066, 61471110), Foundation Project of National Key Laboratory (6142002301, 61420030302), the Distinguished Creative Talent Program of Shenyang(RC170490) and the Fundamental Research Funds for the Central Universities (N172608005, N182608004).
Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10/31
Y1 - 2020/10/31
N2 - Person re-identification (Re-ID) is the task of matching a target person across different cameras, which has drawn extensive attention in computer vision and has become an essential component in the video surveillance system. Despite recent remarkable progress, person re-identification methods are either subject to the power of feature representation, or give equal importance to all examples. To mitigate these issues, we introduce a simple, yet effective, Multi-level and Multi-scale Horizontal Pooling Network (MMHPN) for person re-identification. Concretely, our contributions are three-fold:1) we take partial feature representation into account at different pooling scales and different semantic levels so that various partial information is obtained to form a robust descriptor; 2) we introduce a Part Sensitive Loss (PSL) to reduce the effect of easily classified partition to facilitate training of the person re-identification network, 3) we conduct extensive experimental results using the Market-1501, DukeMTMC-reID and CUHK03 datasets and achieve mAP scores of 83.4%, 75.1% and 65.4% respectively on these challenging datasets.
AB - Person re-identification (Re-ID) is the task of matching a target person across different cameras, which has drawn extensive attention in computer vision and has become an essential component in the video surveillance system. Despite recent remarkable progress, person re-identification methods are either subject to the power of feature representation, or give equal importance to all examples. To mitigate these issues, we introduce a simple, yet effective, Multi-level and Multi-scale Horizontal Pooling Network (MMHPN) for person re-identification. Concretely, our contributions are three-fold:1) we take partial feature representation into account at different pooling scales and different semantic levels so that various partial information is obtained to form a robust descriptor; 2) we introduce a Part Sensitive Loss (PSL) to reduce the effect of easily classified partition to facilitate training of the person re-identification network, 3) we conduct extensive experimental results using the Market-1501, DukeMTMC-reID and CUHK03 datasets and achieve mAP scores of 83.4%, 75.1% and 65.4% respectively on these challenging datasets.
KW - Horizontal pooling network
KW - Multi-level and multi-scale
KW - Part sensitive loss
KW - Person re-identification
UR - https://pure.ulster.ac.uk/en/publications/multi-level-and-multi-scale-horizontal-pooling-network-for-person
UR - http://www.scopus.com/inward/record.url?scp=85089825022&partnerID=8YFLogxK
U2 - 10.1007/s11042-020-09427-y
DO - 10.1007/s11042-020-09427-y
M3 - Article
AN - SCOPUS:85089825022
VL - 79
SP - 28603
EP - 28619
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
SN - 1380-7501
IS - 39-40
ER -