  Unsupervised domain adaptation for person re-identification (Person Re-ID) is
the task of transferring the learned knowledge on the labeled source domain to
the unlabeled target domain. Most of the recent papers that address this
problem adopt an offline training setting. More precisely, the training of the
Re-ID model is done assuming that we have access to the complete training
target domain data set. In this paper, we argue that the target domain
generally consists of a stream of data in a practical real-world application,
where data is continuously increasing from the different network's cameras. The
Re-ID solutions are also constrained by confidentiality regulations stating
that the collected data can be stored for only a limited period, hence the
model can no longer get access to previously seen target images. Therefore, we
present a new yet practical online setting for Unsupervised Domain Adaptation
for person Re-ID with two main constraints: Online Adaptation and Privacy
Protection. We then adapt and evaluate the state-of-the-art UDA algorithms on
this new online setting using the well-known Market-1501, Duke, and MSMT17
benchmarks.
