A Hybrid Deep Model for Person Re-Identification

Abstract

In this study, we present a hybrid model that combines the advantages of the identification, verification and triplet models for person re-identification. Specifically, the proposed model simultaneously uses Online Instance Matching (OIM), verification and triplet losses to train the carefully designed network. Given a triplet images, the model can output the identities of the three input images and the similarity score as well as make the L-2 distance between the mismatched pair larger than the one between the matched pair. Experiments on two benchmark datasets (CUHK01 and Market-1501) show that the proposed method can achieve favorable accuracy while compared with other state of the art methods.