Low-resolution assisted three-stream network for person re-identification

Abstract

In the commonly used datasets of person re-identification, the image quality is not uniform. Most existing methods on person re-identification mainly focus on the challenges caused by occlusion, view and pose variations, ignoring the diversity of person image quality. In this paper, we provide an intuitive solution to address this problem. Specifically, we generate low-resolution images by reducing the resolution of original person images and propose a low-resolution assisted three-stream network (LRAN) to fuse the extracted person features from original RGB images, low-resolution images and greyscale images into a more robust feature as the final person representation. In this way, the model eliminates the impact of image quality differences to some extent. Experimental results demonstrate that the proposed method achieves the state-of-the-art results on Market-1501, DukeMTMC-reID and CUHK03-NP datasets.