Presentation + Paper
28 October 2022 Rapid person re-identification retraining strategy for flexible deployment in new environments
Arthur van Rooijen, Henri Bouma, Jan Baan, Martin van Leeuwen
Author Affiliations +
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.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arthur van Rooijen, Henri Bouma, Jan Baan, and Martin van Leeuwen "Rapid person re-identification retraining strategy for flexible deployment in new environments", Proc. SPIE 12275, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies VI, 122750D (28 October 2022); https://doi.org/10.1117/12.2637415
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Cameras

Databases

Real-time computing

Sensors

Data modeling

Imaging systems

Environmental sensing

Back to Top