Low-Resolution Person Re-identification by a Discriminative Resolution-Invariant Network

Abstract

Person Re-identification (re-id) needs to tackle with the problem of changing resolutions because the pedestrians from surveillance systems or public datasets have low-resolution problem (LR-REID) including low quality, blurry textures and so on, which results in a difficult challenge to extract the identity information under various resolutions. However, most existing re-id models are trained by high-resolution (HR) images, which will achieve poor performance when conducted directly on low-resolution images. In this paper, we propose a novel Discriminative Resolution-invariant Network (DRINet) to explore the subspace where LR and HR features are highly correlated and we can extract discriminant features in the commonly shared feature space. Firstly, we adopt ResNet as the backbone and impose the softmax loss together with the triplet loss to learn distinguishing features. Secondly, we impose the KL divergence loss on the backbone features to minimize the discrepancies between LR and HR features. Finally, we integrate the sparse auto-encoder (SAE) structure to find a subspace which is robust to the resolution variations. Experimental results verify the effectiveness of the DRINet in improving the LR-REID performance and the superiority of the DRINet against the state-of-the-art methods.