To solve the problem of insufficient training samples of existing pedestrian re-identification methods

semantic segmentation is applied to separate the pedestrian area from the background in the sample image. and the generative adversarial network（ GAN） is used to generate the different backgrounds for image scene transition.This mechanism not only expand the existing datasets

but also retain the pedestrian characteristics. The sliding window alignment（ SWA） method based on semantic segmentation is proposed to solve the problem of pedestrian area misalignment in datasets. At the same time

the global feature branch is added to the residual convolutional neural network structure Res Net—50. In the experiment

this method is tested on Market—1501 and Duke MTMCre ID datasets

and achieve accuracy of 91. 4 % and 81. 1 % in rank—1 index.