14 May 2018 Discriminative deep transfer metric learning for cross-scenario person re-identification
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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.
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Tongguang Ni, Zhongbao Zhang, Hongyuan Wang, Shoubing Chen, Cui Jin, "Discriminative deep transfer metric learning for cross-scenario person re-identification", Proc. SPIE 10670, Real-Time Image and Video Processing 2018, 106700P (14 May 2018); doi: 10.1117/12.2309854; https://doi.org/10.1117/12.2309854
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