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

Abstract

A discriminative deep transfer metric learning method called DDTML is proposed for cross-scenario person re-identification (Re-ID). To develop the Re-ID model in a new scenario, a large number of pairwise cross-camera-view person images are deemed necessary. However, this work is very expensive due to both monetary cost and labeling time. In order to solve this problem, a DDTML for cross-scenario Re-ID is proposed using the transferring data in other scenarios to help build a Re-ID model in a new scenario. Specifically, to measure distribution difference across scenarios, a maximum mean discrepancy based on class distribution called MMDCD is proposed by embedding the discriminative information of data into the concept of the maximum mean discrepancy. Unlike most metric learning methods, which usually learn a linear distance to project data into the feature space, DDTML uses a deep neural network to develop the multilayers nonlinear transformations for learning the nonlinear distance metric, while DDTML transfers discriminative information from the source domain to the target domain. By bedding the MMDCD criteria, DDTML minimizes the distribution divergence between the source domain and the target domain. Experimental results on widely used Re-ID datasets show the effectiveness of the proposed classifiers. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.