Improving Person Re-Identification with Temporal Constraints
share
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 Bayesian temporal re-ranking post-processing step, which further adds a 10.03 gain in mAP and 9.95 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
READ FULL TEXT
