  In this paper we introduce an image-based person re-identification dataset
collected across five non-overlapping camera views in the large and busy
airport in Dublin, Ireland. Unlike all publicly available image-based datasets,
our dataset contains timestamp information in addition to frame number, and
camera and person IDs. Also our dataset has been fully anonymized to comply
with modern data privacy regulations. We apply state-of-the-art person
re-identification models to our dataset and show that by leveraging the
available timestamp information we are able to achieve a significant gain of
37.43% in mAP and a gain of 30.22% in Rank1 accuracy. We also propose a
Bayesian temporal re-ranking post-processing step, which further adds a 10.03%
gain in mAP and 9.95% gain in Rank1 accuracy metrics. This work on combining
visual and temporal information is not possible on other image-based person
re-identification datasets. We believe that the proposed new dataset will
enable further development of person re-identification research for challenging
real-world applications. DAA dataset can be downloaded from
https://bit.ly/3AtXTd6
