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Most traditional methods only carry out feature alignment, which ignores the uniqueness of modality differences and is difficult to eliminate the huge differences between RGB and IR. In this paper, a novel AGF network is proposed for RGB\u2010IR re\u2010ID task, which is based on the idea of global and local alignment. The AGF network distinguishes pedestrians in different modalities globally by combining pixel alignment and feature alignment and highlights more structure information of person locally by weighting channels with SE\u2010ResNet\u201050, which has achieved ideal results. It consists of three modules, including alignGAN module (<jats:italic>A<\/jats:italic>), crossmodality paired\u2010images generation module (<jats:italic>G<\/jats:italic>), and feature alignment module (<jats:italic>F<\/jats:italic>). First, at pixel level, the RGB images are converted into IR images through the pixel alignment strategy to directly reduce the crossmodality difference between RGB and IR images. Second, at feature level, crossmodality paired images are generated by exchanging the modality\u2010specific features of RGB and IR images to perform global set\u2010level and fine\u2010grained instance\u2010level alignment. Finally, the SE\u2010ResNet\u201050 network is used to replace the commonly used ResNet\u201050 network. By automatically learning the importance of different channel features, it strengthens the ability of the network to extract more fine\u2010grained structural information of person crossmodalities. Extensive experimental results conducted on SYSU\u2010MM01 dataset demonstrate that the proposed method favorably outperforms state\u2010of\u2010the\u2010art methods. In addition, we evaluate the performance of the proposed method on a stronger baseline, and the evaluation results show that a RGB\u2010IR re\u2010ID method will show better performance on a stronger baseline.<\/jats:p>","DOI":"10.1155\/2022\/4330804","type":"journal-article","created":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T15:50:21Z","timestamp":1641484221000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Crossmodality Person Reidentification Based on Global and Local Alignment"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8069-9119","authenticated-orcid":false,"given":"Qiong","family":"Lou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junfeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4056-9755","authenticated-orcid":false,"given":"Yaguan","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anlin","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,1,6]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"crossref","unstructured":"LiW. 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