  Person re-identification (Re-ID) aims to match a target person across camera
views at different locations and times. Existing Re-ID studies focus on the
short-term cloth-consistent setting, under which a person re-appears in
different camera views with the same outfit. A discriminative feature
representation learned by existing deep Re-ID models is thus dominated by the
visual appearance of clothing. In this work, we focus on a much more difficult
yet practical setting where person matching is conducted over long-duration,
e.g., over days and months and therefore inevitably under the new challenge of
changing clothes. This problem, termed Long-Term Cloth-Changing (LTCC) Re-ID is
much understudied due to the lack of large scale datasets. The first
contribution of this work is a new LTCC dataset containing people captured over
a long period of time with frequent clothing changes. As a second contribution,
we propose a novel Re-ID method specifically designed to address the
cloth-changing challenge. Specifically, we consider that under cloth-changes,
soft-biometrics such as body shape would be more reliable. We, therefore,
introduce a shape embedding module as well as a cloth-elimination
shape-distillation module aiming to eliminate the now unreliable clothing
appearance features and focus on the body shape information. Extensive
experiments show that superior performance is achieved by the proposed model on
the new LTCC dataset. The code and dataset will be available at
https://naiq.github.io/LTCC_Perosn_ReID.html.
