Cross-domain person re-identification using graph convolutional networks

Considering the problem that the person re-identification model trained by the source domain usually can only obtain weak generalization ability in target domain, a method based on graph convolutional networks (GCN) was proposed for cross-domain person re-identification by transferring the ability of integrating neighbor sample information learned from the source domain to target domain.Firstly, a source affinity subgraph was established based on data features of source domain.Then the source affinity subgraph and data features of source domain were taken together as the input of the designed graph convolutional neural network module so as to train the module based on the supervisory information of the source domain.Secondly, after establishing the target affinity subgraph based on data features of target domain, the target affinity subgraph and the trained graph convolution neural network module can be used to realize the purpose of assigning pseudo labels for the target domain data.Lastly, a generalized person re-identification model can be obtained by combining the source domain data and the target domain data.Experiments are constructed on two large public dataset: Market-1501 and DukeMTMC-reID.As shown in the extensive experimental results, compared with the baseline model, the rank-1 accuracy and mean average precision (mAP) on Market-1501 is improved 7.4% and 9.2%, respectively;the rank-1 accuracy and mAP on DukeMTMC-reID is improved 14.2% and 14.9%, respectively. © 2020, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.