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Proceedings Paper

Discriminative deep transfer metric learning for cross-scenario person re-identification
Author(s): Tongguang Ni; Zhongbao Zhang; Hongyuan Wang; Shoubing Chen; Cui Jin
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Paper Abstract

A novel discriminative deep transfer learning method called DDTML is proposed for Cross-scenario Person Reidentification( Re-ID). Using a deep neural network, DDTML learns a set of hierarchical nonlinear transformations for Cross-scenario Person Re-identification by transferring discriminative knowledge from the source domain to the target domain. Meanwhile, taking account of the inherent characteristics of Re-ID data sets, in order to reduce the distribution divergence between the source data and the target data, DDTML minimizes a new maximum mean discrepancy based on Class Distribution called MMDCD at the top layer of the network. Experimental results on widely used Re-identification datasets show the effectiveness of the proposed classifiers.

Paper Details

Date Published: 14 May 2018
PDF: 11 pages
Proc. SPIE 10670, Real-Time Image and Video Processing 2018, 106700P (14 May 2018); doi: 10.1117/12.2309854
Show Author Affiliations
Tongguang Ni, Changzhou Univ. (China)
Zhongbao Zhang, Changzhou Univ. (China)
Hongyuan Wang, Changzhou Univ. (China)
Shoubing Chen, Changzhou Univ. (China)
Cui Jin, Changzhou Univ. (China)

Published in SPIE Proceedings Vol. 10670:
Real-Time Image and Video Processing 2018
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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