Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China.
Xi'an Zhongtie Rail Transit Co., Ltd., Xian, Shaanxi 710000, China.
Comput Intell Neurosci. 2022 Jul 5;2022:4398727. doi: 10.1155/2022/4398727. eCollection 2022.
In real-life scenarios, the accuracy of person re-identification (Re-ID) is subject to the limitation of camera hardware conditions and the change of image resolution caused by factors such as camera focusing errors. People call this problem cross-resolution person Re-ID. In this paper, we improve the recognition accuracy of cross-resolution person Re-ID by enhancing the image enhancement network and feature extraction network. Specifically, we treat cross-resolution person Re-ID as a two-stage task: the first stage is the image enhancement stage, and we propose a Super-Resolution Dual-Stream Feature Fusion sub-network, named SR-DSFF, which contains SR module and DSFF module. The SR-DSFF utilizes the SR module recovers the resolution of the low-resolution (LR) images and then obtains the feature maps of the LR images and super-resolution (SR) images, respectively, through the dual-stream feature fusion with learned weights extracts and fuses feature maps from LR and SR images in the DSFF module. At the end of SR-DSFF, we set a transposed convolution to visualize the feature maps into images. The second stage is the feature acquisition stage. We design a global-local feature extraction network guided by human pose estimation, named FENet-ReID. The FENet-ReID obtains the final features through multistage feature extraction and multiscale feature fusion for the Re-ID task. The two stages complement each other, making the final pedestrian feature representation has the advantage of accurate identification compared with other methods. Experimental results show that our method improves significantly compared with some state-of-the-art methods.
在实际场景中，人像重识别（Re-ID）的准确性受到相机硬件条件的限制，以及由于相机对焦误差等因素导致的图像分辨率变化的影响。人们将这个问题称为跨分辨率人像 Re-ID。在本文中，我们通过增强图像增强网络和特征提取网络来提高跨分辨率人像 Re-ID 的识别精度。具体来说，我们将跨分辨率人像 Re-ID 视为一个两阶段任务：第一阶段是图像增强阶段，我们提出了一个超分辨率双流特征融合子网络，称为 SR-DSFF，它包含 SR 模块和 DSFF 模块。SR-DSFF 利用 SR 模块恢复低分辨率（LR）图像的分辨率，然后通过双流特征融合分别获取 LR 图像和超分辨率（SR）图像的特征图，通过 DSFF 模块中的学习权重提取和融合 LR 和 SR 图像的特征图。在 SR-DSFF 的最后，我们设置一个转置卷积将特征图可视化成图像。第二阶段是特征采集阶段。我们设计了一个基于人体姿态估计的全局-局部特征提取网络，称为 FENet-ReID。FENet-ReID 通过多阶段特征提取和多尺度特征融合为 Re-ID 任务获取最终的特征。这两个阶段相互补充，使得最终的行人特征表示具有比其他方法更准确的识别优势。实验结果表明，与一些最先进的方法相比，我们的方法有了显著的提高。
