Cross-modal Pedestrian Re-identification Based on Generative Confrontation Network

Abstract

Pedestrian re-recognition is a very important research direction in video surveillance. With the emphasis on night video surveillance, pedestrian re-recognition is also being studied from a single mode to a cross-mode direction. Since the images taken by the camera at night are generally divided into two types, thermal imaging and infrared images, corresponding to the RegDB data set and the SYSU-MM01 data set respectively. In order to make the trained model have good performance in both data sets, GAN network is used in this article. The visible light image is generated by CycleGAN to generate the corresponding infrared image, and then the infrared image is generated by PTGAN to generate a thermal imaging style image. Then input the image into the single-stream network for training and learning, and finally optimize the network in an end-to-end manner.