Title
Rapid person re-identification retraining strategy for flexible deployment in new environments
Author
van Rooijen, A.L.
Bouma, H.
Baan, J.
van Leeuwen, M.C.
Publication year
2022
Abstract
Person re-identification (Re-ID) can be used to find the owner of lost luggage, to find suspects after a terrorist attack, or to fuse multiple sensors. Common state-of-the-art deep-learning technology performs well on a large public dataset but it does not generalize well to other environments, which makes it less suitable for practical applications. In this paper, we present and evaluate a new strategy for rapid Re-ID retraining to increase flexibility for deployment in new environments. In addition, we pay special attention to make our method work with anonymized data due to the sensitive nature of the collected data. A training set with anonymized snippets is automatically collected using additional cameras and person tracking. The evaluation results show that this rapid training approach obtains high performance scores.
Subject
Re-identification
Surveillance
Threat detection
Forensics
Counter-terrorism
To reference this document use:
http://resolver.tudelft.nl/uuid:0f01edde-8aac-4c2b-82c1-a549487cd134
TNO identifier
977932
Source
SPIE Proceedings Volume 12275, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies VI; 122750D
Document type
conference paper