{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:38:21Z","timestamp":1777696701536,"version":"3.51.4"},"reference-count":50,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2024,11,15]]},"abstract":"<jats:p>Person re-identification (ReID) is widely used in intelligent security, monitoring, criminal investigation and other fields. Aiming at the problems of local occlusion, scale misalignment and attitude change of pedestrian images in actual scenes, we propose a Multi-local Feature and Attention fused network (MFA) used for person re-identification task. Firstly, Channel Point Affinity Attention module (CPAA) is embedded in the backbone network to enhance the ability of the network for extracting local details. The feature map output from the backbone network is horizontally segmented into four local feature maps, and further four branch networks are concatenated to the feature map of the backbone network. The four local feature maps are used to guide the four branch networks to pay more attention on different areas of pedestrians through Global Local Aligned loss (GLA) function. Finally, the pedestrian feature vector containing multi-local features is obtained. The mAP of the network on Market-1501, DukeMTMC-reID,CUHK03 and MSMT17 datasets were 88.6%, 81.4%, 79.5% and 64.7%, and the Rank-1 was 95.8%, 90.1%, 81.2% and 84.1% respectively. In addition, the model also obtained 73.2% and 68.1% of Rank-1 on partial dataset Patial-REID and Patial-iLIDS, respectively. Recently, The MFA model parameter is 28.3M and the inference efficiency is approximately 32\u00a0fps to an image with a resulation of 256 \u00d7 128. Compared with other ReID methods, our proposed methods achieved a competitive performance for ReID task. The code was available at github:git@github.com:ISCLab-Bistu\/MFA.git.<\/jats:p>","DOI":"10.3233\/ida-230392","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T10:34:33Z","timestamp":1707474873000},"page":"1679-1695","source":"Crossref","is-referenced-by-count":1,"title":["Multiple-local feature and attention fused person re-identification method"],"prefix":"10.1177","volume":"28","author":[{"given":"Mingxin","family":"Yu","sequence":"first","affiliation":[{"name":"Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"You","sequence":"additional","affiliation":[{"name":"Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinglong","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Precision Instrument, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenshuai","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Precision Instrument, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-230392_ref1","first-page":"152","article-title":"Deepreid: Deep filter pairing neural network for person re-identification","author":"Li","year":"2014","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"issue":"8","key":"10.3233\/IDA-230392_ref2","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"10.3233\/IDA-230392_ref3","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1561\/2200000019","article-title":"Metric learning: A survey","volume":"5","author":"Kulis","year":"2013","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"key":"10.3233\/IDA-230392_ref4","first-page":"3702","article-title":"Omni-scale feature learning for person re-identification","author":"Zhou","year":"2019","journal-title":"Proceedings of the IEEE, CVF International Conference on Computer Vision"},{"key":"10.3233\/IDA-230392_ref5","doi-asserted-by":"crossref","unstructured":"H. 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