Unsupervised domain adaptation pedestrian re-identification based on an improved dissimilarity space

Abstract

Pedestrian re-identification is a key and challenging research topic in intelligent security applications. Because of the need for large-scale manual labeling, pedestrian re-identification methods based on supervised learning cannot be widely used in practical applications. Research on unsupervised pedestrian re-identification has therefore become a hot topic. The DMMD model published in ECCV2020 successfully applied the dissimilarity space to unsupervised pedestrian re-identification. Based on the analysis of the deficiencies of DMMD, a new strong baseline model is composed, an improved dissimilarity space is constructed, a new transfer learning optimization method is proposed, and a time-space-appearance constraint is proposed. The test results show that the R1 accuracy of our model is improved by 21.4% compared with DMMD in the experiment from Market1501 to DukeMTMC.