TY - GEN
T1 - Deep full-scaled metric learning for pedestrians re-identification
T2 - Joint Workshop of the 4th Workshop on Affective Social Multimedia Computing and 1st Multi-Modal Affective Computing of Large-Scale Multimedia Data Workshop, ASMMC-MMAC 2018
AU - Huang, Wei
AU - Luo, Mingyuan
AU - Zhang, Peng
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/19
Y1 - 2018/10/19
N2 - In this study, a new full-scaled deep discriminant model is proposed to tackle the re-identification (re-id) problem of pedestrian targets, which aims to identify pedestrian targets within a network of cameras with non-overlapping fields of view and is pre-requisite in multi-camera-based affective computing. The new full-scaled model is realized by taking concepts of depth, width, and cardinality simultaneously into consideration, and the challenging re-id problem in this study is further tackled via a novel deep semi-supervised metric learning method based on the full-scaled model. Additionally, both the conventional stochastic gradient descent algorithm and an alternative more efficient proximal gradient descent algorithm are derived to realize the new deep metric learning method. For experimental evaluations, the novel full-scaled deep metric learning method has been compared with 9 other popular re-id methods based on 3 well-known databases. Comprehensive statistical analyses suggest the superiority of the new method when handling the balance learning problem in the re-id task.
AB - In this study, a new full-scaled deep discriminant model is proposed to tackle the re-identification (re-id) problem of pedestrian targets, which aims to identify pedestrian targets within a network of cameras with non-overlapping fields of view and is pre-requisite in multi-camera-based affective computing. The new full-scaled model is realized by taking concepts of depth, width, and cardinality simultaneously into consideration, and the challenging re-id problem in this study is further tackled via a novel deep semi-supervised metric learning method based on the full-scaled model. Additionally, both the conventional stochastic gradient descent algorithm and an alternative more efficient proximal gradient descent algorithm are derived to realize the new deep metric learning method. For experimental evaluations, the novel full-scaled deep metric learning method has been compared with 9 other popular re-id methods based on 3 well-known databases. Comprehensive statistical analyses suggest the superiority of the new method when handling the balance learning problem in the re-id task.
KW - Deep full-scaled metric learning
KW - Multi-camera affective computing
KW - Pedestrians re-identification
UR - http://www.scopus.com/inward/record.url?scp=85061701382&partnerID=8YFLogxK
U2 - 10.1145/3267935.3267937
DO - 10.1145/3267935.3267937
M3 - 会议稿件
AN - SCOPUS:85061701382
T3 - ASMMC-MMAC 2018 - Proceedings of the Joint Workshop of the 4th Workshop on Affective Social Multimedia Computing and 1st Multi-Modal Affective Computing of Large-Scale Multimedia Data, Co-located with MM 2018
SP - 47
EP - 53
BT - ASMMC-MMAC 2018 - Proceedings of the Joint Workshop of the 4th Workshop on Affective Social Multimedia Computing and 1st Multi-Modal Affective Computing of Large-Scale Multimedia Data, Co-located with MM 2018
PB - Association for Computing Machinery, Inc
Y2 - 26 October 2018
ER -