  Person re-identification (ReID) aims to match people across multiple
non-overlapping video cameras deployed at different locations. To address this
challenging problem, many metric learning approaches have been proposed, among
which triplet loss is one of the state-of-the-arts. In this work, we explore
the margin between positive and negative pairs of triplets and prove that large
margin is beneficial. In particular, we propose a novel multi-stage training
strategy which learns incremental triplet margin and improves triplet loss
effectively. Multiple levels of feature maps are exploited to make the learned
features more discriminative. Besides, we introduce global hard identity
searching method to sample hard identities when generating a training batch.
Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our
approach yields a performance boost and outperforms most existing
state-of-the-art methods.
