Tang Yingzhi, Yang Xi, Jiang Xinrui, Wang Nannan, Gao Xinbo
IEEE Trans Cybern. 2022 Nov;52(11):12016-12027. doi: 10.1109/TCYB.2021.3077500. Epub 2022 Oct 17.
Person reidentification (Re-ID) aims at recognizing the same identity across different camera views. However, the cross resolution of images [high resolution (HR) and low resolution (LR)] is unavoidable in a realistic scenario due to the various distances among cameras and pedestrians of interest, thus leading to cross-resolution person Re-ID problems. Recently, most cross-resolution person Re-ID methods focus on solving the resolution mismatch problem, while the distribution mismatch between HR and LR images is another factor that significantly impacts the person Re-ID performance. In this article, we propose a dually distribution pulling network (DDPN) to tackle the distribution mismatch problem. DDPN is composed of two modules, that is: 1) super-resolution module and 2) person Re-ID module. They attempt to pull the distribution of LR images closer to the distribution of HR images from image and feature aspects, respectively, through optimizing the maximum mean discrepancy losses. Extensive experiments have been conducted on three benchmark datasets and the results demonstrate the effectiveness of DDPN. Remarkably, DDPN shows a great advantage when compared to the state-of-the-art methods, for instance, we achieve rank-1 accuracy of 76.9% on VR-Market1501, which outperforms the best existing cross-resolution person Re-ID method by 10%.
行人重识别（Re-ID）旨在跨不同摄像头视角识别同一身份。然而，在现实场景中，由于摄像头与感兴趣行人之间存在不同距离，图像的跨分辨率（高分辨率（HR）和低分辨率（LR））问题不可避免，从而导致跨分辨率行人重识别问题。最近，大多数跨分辨率行人重识别方法专注于解决分辨率不匹配问题，而HR图像和LR图像之间的分布不匹配是另一个显著影响行人重识别性能的因素。在本文中，我们提出了一种双分布拉取网络（DDPN）来解决分布不匹配问题。DDPN由两个模块组成，即：1）超分辨率模块和2）行人重识别模块。它们分别尝试通过优化最大均值差异损失，从图像和特征方面将LR图像的分布拉近到HR图像的分布。我们在三个基准数据集上进行了广泛实验，结果证明了DDPN的有效性。值得注意的是，与现有最先进方法相比，DDPN具有很大优势，例如，我们在VR-Market1501上实现了76.9%的Rank-1准确率，比现有的最佳跨分辨率行人重识别方法高出10%。
