Abstract
As the key technology of automatic video surveillance, person re-identfication (Re-ID) has attracted widespread interest and has been applied in many applications. Most of existing Re-ID methods usually assume that pedestrian images taken from different cameras have uniform resolutions. However, in many practical scenes, due to variations of distances between person-cameras as well as the deployment setting of cameras, the resolution of the pedestrian images is usually different. Such situation will be resulting in resolution mismatch problem. If we directly match pedestrian image with different resolutions, the performance of Re-ID will be affected adversely because of the discrepancy of information amount. To tackle this issue, one potential solution is to combine the super-resolution (SR) technology with Re-ID method. In this paper, we propose a super-resolution GAN (SR-GAN) based Multi-scale Deep Feature Representation (MFR-GAN) framework for Low Resolution person re-identification, which aims to optimize the super-resolution of image and pedestrian matching. We first design three cascaded SR-GANs to increase the resolution of person images with different upscaling factor and then introduce a re-identification network after each SR-GAN to strengthen the representation capability of image features. Experiments on two synthetic datasets and a common Re-ID dataset confirm that our method (MFR-GAN) can solve the resolution mismatch problem effectively.
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URL
https://arxiv.org/abs/2008.10329
