  Unsupervised cross-domain person re-identification (Re-ID) faces two key
issues. One is the data distribution discrepancy between source and target
domains, and the other is the lack of labelling information in target domain.
They are addressed in this paper from the perspective of representation
learning. For the first issue, we highlight the presence of camera-level
sub-domains as a unique characteristic of person Re-ID, and develop
camera-aware domain adaptation to reduce the discrepancy not only between
source and target domains but also across these sub-domains. For the second
issue, we exploit the temporal continuity in each camera of target domain to
create discriminative information. This is implemented by dynamically
generating online triplets within each batch, in order to maximally take
advantage of the steadily improved feature representation in training process.
Together, the above two methods give rise to a novel unsupervised deep domain
adaptation framework for person Re-ID. Experiments and ablation studies on
benchmark datasets demonstrate its superiority and interesting properties.
