Aiming at the problem that the current network is difficult to deal with various corrupted pedestrian images and easily loses cross-dimensional information,a pedestrian re-identification (ReID) method based on style normalization and global attention is proposed for corrupted images.The method filters out style changes in the domain by smooth maximum unit-style normalization and restitution (SM-SNR) module in the instance normalization (IN), and at the same time smooth maximum unit (SMU) enables the module to more fully extract pedestrian-related features from the deleted information and restore them to the network,so as to alleviate the style difference caused by corrupted images.In addition,the global attention mechanism (GAM) captures the salient pedestrian features in three dimensions by focusing on the interaction between the channel and the space,reducing the loss of cross-dimensional information.Finally,the recognition ability of the model in recognizing pedestrian corrupted images is effectively improved,and the competitiveness on clean datasets is retained.The experimental results show that the indicators of the algorithm on the corrupted test set has significant advantages compared with the current mainstream algorithms.Among these algorithms,the result of comparison with the 2021 CIL model using the CUHK03 dataset is that:On Corrupted Eval,R-1,mAP and mINP increase by 15.18%,15.75% and 11.65% respectively;on Clean Eval,R-1 and mINP only decrease by 0.24%,0.75%, and mAP increased by 0.25%.