| Citation: | ZHANG Xiaowei, LYU Mingqiang, LI huiet al. Cross-domain person re-identification based on partial semantic feature invariance[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1682-1690. doi: 10.13700/j.bh.1001-5965.2020.0072(in Chinese)
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Cross-domain is one of the main challenges of person re-identification which is an important investigative method in criminal investigation cases, and that restricts the practical application of re-identification. In this paper, cross-domain invariance feature model based on pedestrian partial semantics is learned from the labeled source domain and the unlabeled target domain. First, features of person parts are learned by supervised learning with only pedestrian signs without labels of parts, and pedestrian parts are aligned by unsupervised learning in the source and target domains. Second, based on the aligned global and local features, feature template pooling is introduced to store the aligned global and local partial features of the target domain, and cross-domain invariance loss function is designed for feature invariance constraints to improve the cross-domain adaptability of person re-identification. Finally, verification experiments of cross-domain person re-identification are conducted on the Market-1501, DukeMTMC-reID and MSMT17 datasets. The experiment results show that the proposed method achieves significant performance improvements in cross-domain person re-identification.
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