Journal
NEUROCOMPUTING
Volume 324, Issue -, Pages 69-75Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2018.03.073
Keywords
Convolutional neural networks; Person re-identification; Center loss; Triplet loss; Identification loss
Categories
Funding
- National Science Foundation of China [61472280, 61732012, 6152010 600 6, 31571364, 61672203, 61472173, 61572447, 61672382, 61772370, 61772357]
- China Postdoctoral Science Foundation Grant [2016M601646, 2017M611619]
- BAGUI Scholar Program of Guangxi Zhuang Autonomous Region of China
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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. (C) 2018 Elsevier B.V. All rights reserved.
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