Article
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Preserved in Portico This version is not peer-reviewed
A generative approach to person reidentification
Version 1
: Received: 22 December 2023 / Approved: 22 December 2023 / Online: 25 December 2023 (09:18:36 CET)
A peer-reviewed article of this Preprint also exists.
Asperti, A.; Fiorilla, S.; Orsini, L. A Generative Approach to Person Reidentification. Sensors 2024, 24, 1240. Asperti, A.; Fiorilla, S.; Orsini, L. A Generative Approach to Person Reidentification. Sensors 2024, 24, 1240.
Abstract
Person Re-identification is the task of recognizing comparable subjects across a network of nonoverlapping cameras. This is typically achieved by extracting from the source image a vector of characteristic features of the specific person captured by the camera. Learning a good set of robust, invariant and discriminative features is a complex task, often leveraging contrastive learning. In this article, we explore a different approach, learning the representation of an individual as the conditioning information required to generate images of the specific person starting from random noise. In this way we decouple the identity of the individual from any other information relative to a specific instance (pose, background, etc.), allowing interesting transformations from one identity to another. As generative models, we use the recent diffusion models that have already proven their sensibility to conditioning in many different contexts. The results presented in this article serve as a proof-of-concept. While our current performance on common benchmarks is lower than state-of-the-art techniques, the approach is intriguing and rich of innovative insights, suggesting a wide range of potential improvements along various lines of investigation.
Keywords
Person Re-identification, Image Generation, Diffusion Models; Latent Space, Representation Learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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