  While metric learning is important for Person re-identification (RE-ID), a
significant problem in visual surveillance for cross-view pedestrian matching,
existing metric models for RE-ID are mostly based on supervised learning that
requires quantities of labeled samples in all pairs of camera views for
training. However, this limits their scalabilities to realistic applications,
in which a large amount of data over multiple disjoint camera views is
available but not labelled. To overcome the problem, we propose unsupervised
asymmetric metric learning for unsupervised RE-ID. Our model aims to learn an
asymmetric metric, i.e., specific projection for each view, based on asymmetric
clustering on cross-view person images. Our model finds a shared space where
view-specific bias is alleviated and thus better matching performance can be
achieved. Extensive experiments have been conducted on a baseline and five
large-scale RE-ID datasets to demonstrate the effectiveness of the proposed
model. Through the comparison, we show that our model works much more suitable
for unsupervised RE-ID compared to classical unsupervised metric learning
models. We also compare with existing unsupervised RE-ID methods, and our model
outperforms them with notable margins. Specifically, we report the results on
large-scale unlabelled RE-ID dataset, which is important but unfortunately less
concerned in literatures.
