  Person re-identification (re-ID) in the scenario with large spatial and
temporal spans has not been fully explored. This is partially because that,
existing benchmark datasets were mainly collected with limited spatial and
temporal ranges, e.g., using videos recorded in a few days by cameras in a
specific region of the campus. Such limited spatial and temporal ranges make it
hard to simulate the difficulties of person re-ID in real scenarios. In this
work, we contribute a novel Large-scale Spatio-Temporal LaST person re-ID
dataset, including 10,862 identities with more than 228k images. Compared with
existing datasets, LaST presents more challenging and high-diversity re-ID
settings, and significantly larger spatial and temporal ranges. For instance,
each person can appear in different cities or countries, and in various time
slots from daytime to night, and in different seasons from spring to winter. To
our best knowledge, LaST is a novel person re-ID dataset with the largest
spatio-temporal ranges. Based on LaST, we verified its challenge by conducting
a comprehensive performance evaluation of 14 re-ID algorithms. We further
propose an easy-to-implement baseline that works well on such challenging re-ID
setting. We also verified that models pre-trained on LaST can generalize well
on existing datasets with short-term and cloth-changing scenarios. We expect
LaST to inspire future works toward more realistic and challenging re-ID tasks.
More information about the dataset is available at
https://github.com/shuxjweb/last.git.
