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Improving person re-Identification with temporal constraints

Dietlmeier, Julia orcid logoORCID: 0000-0001-9980-0910, Hu, Feiyan orcid logoORCID: 0000-0001-7451-6438, Ryan, Frances, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477 (2022) Improving person re-Identification with temporal constraints. In: Real World Surveillance: Applications and Challenges (RWS) workshop at WACV 2022, 8 Jan 2022, Waikoloa, Hawaii.

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.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Subjects:Computer Science > Algorithms
Computer Science > Artificial intelligence
Computer Science > Information retrieval
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2022. . Computer Vision Foundation (CVF).
Publisher:Computer Vision Foundation (CVF)
Official URL:https://openaccess.thecvf.com/content/WACV2022W/RW...
Copyright Information:© 2021 The Authors. Open Access.
ID Code:26478
Deposited On:06 Jan 2022 17:23 by Julia Dietlmeier . Last Modified 06 Jan 2022 17:23

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