Discriminative deep transfer metric learning for cross-scenario person re-identification

Tongguang Ni; Zhongbao Zhang; Hongyuan Wang; Shoubing Chen; Cui Jin

doi:10.1117/12.2309854

14 May 2018 Discriminative deep transfer metric learning for cross-scenario person re-identification

Tongguang Ni, Zhongbao Zhang, Hongyuan Wang, Shoubing Chen, Cui Jin

Proceedings Volume 10670, Real-Time Image and Video Processing 2018; 106700P (2018) https://doi.org/10.1117/12.2309854
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

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.

Citation Download Citation

Tongguang Ni, Zhongbao Zhang, Hongyuan Wang, Shoubing Chen, and 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); https://doi.org/10.1117/12.2309854

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KEYWORDS

Data modeling

Cameras

Distance measurement

Neural networks

Surveillance systems

Visualization

Computer vision technology

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