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Deep Learning for Person Reidentification Using Support Vector Machines
Joint Authors
Tang, Zhen-min
Xu, Mengyu
Yao, Yazhou
Yao, Lingxiang
Liu, Huafeng
Xu, Jingsong
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-10
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
Due to the variations of viewpoint, pose, and illumination, a given individual may appear considerably different across different camera views.
Tracking individuals across camera networks with no overlapping fields is still a challenging problem.
Previous works mainly focus on feature representation and metric learning individually which tend to have a suboptimal solution.
To address this issue, in this work, we propose a novel framework to do the feature representation learning and metric learning jointly.
Different from previous works, we represent the pairs of pedestrian images as new resized input and use linear Support Vector Machine to replace softmax activation function for similarity learning.
Particularly, dropout and data augmentation techniques are also employed in this model to prevent the network from overfitting.
Extensive experiments on two publically available datasets VIPeR and CUHK01 demonstrate the effectiveness of our proposed approach.
American Psychological Association (APA)
Xu, Mengyu& Tang, Zhen-min& Yao, Yazhou& Yao, Lingxiang& Liu, Huafeng& Xu, Jingsong. 2017. Deep Learning for Person Reidentification Using Support Vector Machines. Advances in Multimedia،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1122389
Modern Language Association (MLA)
Xu, Mengyu…[et al.]. Deep Learning for Person Reidentification Using Support Vector Machines. Advances in Multimedia No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1122389
American Medical Association (AMA)
Xu, Mengyu& Tang, Zhen-min& Yao, Yazhou& Yao, Lingxiang& Liu, Huafeng& Xu, Jingsong. Deep Learning for Person Reidentification Using Support Vector Machines. Advances in Multimedia. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1122389
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1122389