TY - GEN
T1 - Multi-view Based Pose Alignment Method for Person Re-identification
AU - Zhang, Yulei
AU - Zhao, Qingjie
AU - Li, You
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2020.
PY - 2020
Y1 - 2020
N2 - This paper proposes a Multi-View based Pose Alignment (MVPA) method for person re-identification (re-id). Most recent methods solve re-id as a matching process based on single image. However, when poses vary or viewpoints change, the performance seriously deteriorates. This paper aims to learn a representation insensitive to view and pose. Specifically, we establish a set of Multi-view based Person Pose Templates (MPPT) and propose a Pose-Guided Person image Generation (iPG2) model to synthesize multi-view and uniform-pose based images. The representation learned from multi-view images can significantly enhances the accuracy of re-id. We evaluate our method on two popular datasets, i.e., Market-1501 and DukeMTMC-reID. The results show that our framework promotes the performance of re-id a lot and surpass other methods.
AB - This paper proposes a Multi-View based Pose Alignment (MVPA) method for person re-identification (re-id). Most recent methods solve re-id as a matching process based on single image. However, when poses vary or viewpoints change, the performance seriously deteriorates. This paper aims to learn a representation insensitive to view and pose. Specifically, we establish a set of Multi-view based Person Pose Templates (MPPT) and propose a Pose-Guided Person image Generation (iPG2) model to synthesize multi-view and uniform-pose based images. The representation learned from multi-view images can significantly enhances the accuracy of re-id. We evaluate our method on two popular datasets, i.e., Market-1501 and DukeMTMC-reID. The results show that our framework promotes the performance of re-id a lot and surpass other methods.
KW - Generative Adversarial Networks
KW - Person re-identification
KW - Pose Alignment
UR - http://www.scopus.com/inward/record.url?scp=85071503295&partnerID=8YFLogxK
U2 - 10.1007/978-981-32-9050-1_50
DO - 10.1007/978-981-32-9050-1_50
M3 - Conference contribution
AN - SCOPUS:85071503295
SN - 9789813290495
T3 - Lecture Notes in Electrical Engineering
SP - 439
EP - 447
BT - Proceedings of 2019 Chinese Intelligent Automation Conference
A2 - Deng, Zhidong
PB - Springer Verlag
T2 - Chinese Intelligent Automation Conference, CIAC 2019
Y2 - 20 September 2019 through 22 September 2019
ER -