Cycle GAN-MF: A Cycle-consistent Generative Adversarial Network Based on Multifeature Fusion for Pedestrian Re-recognition

Abstract

In pedestrian re-recognition, the traditional pedestrian re-recognition method will be affected by the changes of background, veil, clothing and so on, which will make the recognition effect decline. In order to reduce the impact of background, veil, clothing and other changes on the recognition effect, this paper proposes a pedestrian re-recognition method based on the cycle-consistent generative adversarial network and multifeature fusion. By comparing the measured distance between two pedestrians, pedestrian re-recognition is accomplished. Firstly, this paper uses Cycle GAN to transform and expand the data set, so as to reduce the influence of pedestrian posture changes as much as possible. The method consists of two branches: global feature extraction and local feature extraction. Then the global feature and local feature are fused. The fused features are used for comparison measurement learning, and the similarity scores are calculated to sort the samples. A large number of experimental results on large data sets CUHK03 and VIPER show that this new method reduces the influence of background, veil, clothing and other changes on the recognition effect.