Person re-identification with intra-domain similarity grouping based on semantic fusion - Qiqi KOU; Ji HUANG; Deqiang CHENG; Yunlong LI; Jianying ZHANG (Tongxin xuebao)
Unsupervised cross-domain person re-identification aims to adapt a model trained on a labeled source-domain dataset to a target-domain dataset.However, the cluster-based unsupervised cross-domain pedestrian re-identification algorithm often generates noise due to the different input pedestrian pictures during the network feature learning process, which affects the clustering results.To solve this problem, An intra-domain similarity grouping pedestrian re-identification network based on semantic fusion was proposed.Firstly, a semantic fusion layer was added on the basis of the Baseline network, and the semantic fusion of similar features was performed on the intermediate feature maps from the two aspects of space and channel in turn, so as to improve the adaptive perception ability of the network.In addition, by making full use of the fine-grained information of intra-domain similarity, the network’s clustering accuracy of global and local features was improved.Experiments were carried out on three public datasets, DukeMTMC-ReID, Market1501, MSMT17, and the results demonstrate that the mAP and Rank recognition accuracy are significantly improved compared with recent unsupervised cross-domain person re-identification algorithms.