Easy Identification from Better Constraints: Multi-Shot Person Re-Identification
from Reference Constraints
Abstract
Multi-shot person re-identification (MsP-RID) utilizes
multiple images from the same person to facilitate identi-
fication. Considering the fact that motion information may
not be discriminative nor reliable enough for MsP-RID, this
paper is focused on handling the large variations in the
visual appearances through learning discriminative visual
metrics for identification. Existing metric learning-based
methods usually exploit pair-wise or triple-wise similarity constraints, that generally demands intensive optimization in metric learning, or leads to degraded performances
by using sub-optimal solutions. In addition, as the training data are significantly imbalanced, the learning can be
largely dominated by the negative pairs and thus produces
unstable and non-discriminative results. In this paper, we
propose a novel type of similarity constraint. It assigns
the sample points to a set of reference points to produce
a linear number of reference constraints. Several optimal
transport-based schemes for reference constraint generation are proposed and studied. Based on those constraints,
by utilizing a typical regressive metric learning model, the
closed-form solution of the learned metric can be easily
obtained. Extensive experiments and comparative studies
on several public MsP-RID benchmarks have validated the
effectiveness of our method and its significant superiority
over the state-of-the-art MsP-RID methods in terms of both
identification accuracy and running speed