Paper Info
Title
A novel deep model with multi-loss and efficient training for person re-identification.
Abstract
The purpose of Person re-identification (PReID) is to identify the same individual from the non-overlapping cameras, the task has been greatly promoted by the deep learning system. In this study, we review two widely-used CNN frameworks in the PReID community: identification model and triplet model. We provide a comprehensive overview of the advantages and limitations of the two models and present a hybrid model that combines the advantages of both identification and triplet models. Specifically, the proposed model employs triplet loss, identification loss and center loss to simultaneously train the carefully designed network. Furthermore, the dropout scheme is adopted by its identification subnetwork. Given a triplet unit images, the model can output the identities of the three input images and force the Euclidean distance between the mismatched pairs to be larger than those between the matched pairs as well as reduce the variance of the same class at the same time. Extensive comparative experiments on three PReID benchmark datasets (CUHK01, CUHK03, Market-1501) show that our proposed architecture outperforms many state of the art methods in most cases.
Year
DOI
Venue
2019
10.1016/j.neucom.2018.03.073
Neurocomputing
Keywords
Field
DocType
Convolutional neural networks,Person re-identification,Center loss,Triplet loss,Identification loss
Architecture,Pattern recognition,Euclidean distance,Artificial intelligence,Deep learning,Subnetwork,Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
324
0925-2312
1
PageRank 
References 
Authors
0.35
35
6
Name
Order
Citations
PageRank
Di Wu193.88
Si-Jia Zheng291.85
Wenzheng Bao32810.40
Xiao-Ping Zhang492.89
Chang-an Yuan5859.88
De-Shuang Huang65532357.50