TY - GEN
T1 - Visible thermal person re-identification via dual-constrained top-ranking
AU - Ye, Mang
AU - Wang, Zheng
AU - LAN, Xiangyuan
AU - YUEN, Pong Chi
N1 - Funding Information:
This work is partially supported by Hong Kong RGC General Research Fund HKBU (12202514), and National Natural Science Foundation of China (61562048).
PY - 2018/7
Y1 - 2018/7
N2 - Cross-modality person re-identification between the thermal and visible domains is extremely important for night-time surveillance applications. Existing works in this filed mainly focus on learning sharable feature representations to handle the cross-modality discrepancies. However, besides the cross-modality discrepancy caused by different camera spectrums, visible thermal person re-identification also suffers from large cross-modality and intra-modality variations caused by different camera views and human poses. In this paper, we propose a dual-path network with a novel bi-directional dual-constrained top-ranking loss to learn discriminative feature representations. It is advantageous in two aspects: 1) end-to-end feature learning directly from the data without extra metric learning steps, 2) it simultaneously handles the cross-modality and intra-modality variations to ensure the discriminability of the learnt representations. Meanwhile, identity loss is further incorporated to model the identity-specific information to handle large intra-class variations. Extensive experiments on two datasets demonstrate the superior performance compared to the state-of-the-arts.
AB - Cross-modality person re-identification between the thermal and visible domains is extremely important for night-time surveillance applications. Existing works in this filed mainly focus on learning sharable feature representations to handle the cross-modality discrepancies. However, besides the cross-modality discrepancy caused by different camera spectrums, visible thermal person re-identification also suffers from large cross-modality and intra-modality variations caused by different camera views and human poses. In this paper, we propose a dual-path network with a novel bi-directional dual-constrained top-ranking loss to learn discriminative feature representations. It is advantageous in two aspects: 1) end-to-end feature learning directly from the data without extra metric learning steps, 2) it simultaneously handles the cross-modality and intra-modality variations to ensure the discriminability of the learnt representations. Meanwhile, identity loss is further incorporated to model the identity-specific information to handle large intra-class variations. Extensive experiments on two datasets demonstrate the superior performance compared to the state-of-the-arts.
UR - https://www.ijcai.org/proceedings/2018/
UR - http://www.scopus.com/inward/record.url?scp=85055087985&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2018/152
DO - 10.24963/ijcai.2018/152
M3 - Conference contribution
AN - SCOPUS:85055087985
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 1092
EP - 1099
BT - Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
A2 - Lang, Jerome
PB - International Joint Conferences on Artificial Intelligence
T2 - 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
Y2 - 13 July 2018 through 19 July 2018
ER -