{"id":32660,"date":"2026-01-21T17:02:24","date_gmt":"2026-01-21T17:02:24","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/enhancing-long-term-re-identification-robustness-using-synthetic-data-a-comparative-analysis\/"},"modified":"2026-06-08T13:21:24","modified_gmt":"2026-06-08T13:21:24","slug":"enhancing-long-term-re-identification-robustness-using-synthetic-data-a-comparative-analysis","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/enhancing-long-term-re-identification-robustness-using-synthetic-data-a-comparative-analysis\/","title":{"rendered":"Enhancing Long-Term Re-Identification Robustness Using Synthetic Data: A Comparative Analysis"},"content":{"rendered":"<p>This contribution explores the impact of synthetic training data usage and the prediction of material wear and aging in the context of re-identification. Different experimental setups and gallery set expanding strategies are tested, analyzing their impact on performance over time for aging re-identification subjects. Using a continuously updating gallery, we were able to increase our mean Rank-1 accuracy by 24\\%, as material aging was taken into account step by step. In addition, using models trained with 10\\% artificial training data, Rank-1 accuracy could be increased by up to 13\\%, in comparison to a model trained on only real-world data, significantly boosting generalized performance on hold-out data. Finally, this work introduces a novel, open-source re-identification dataset, pallet-block-2696. This dataset contains 2,696 images of Euro pallets, taken over a period of 4 months. During this time, natural aging processes occurred and some of the pallets were damaged during their usage. These wear and tear processes significantly changed the appearance of the pallets, providing a dataset that can be used to generate synthetically aged pallets or other wooden materials.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This contribution explores the impact of synthetic training data usage and the prediction of material wear and aging in the context of re-identification. Different experimental setups and gallery set expanding strategies are tested, analyzing their impact on performance over time for aging re-identification subjects. Using a continuously updating gallery, we were able to increase our mean Rank-1 accuracy by 24\\%, as material aging was taken into account step by step. [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32660","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32660","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":1,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32660\/revisions"}],"predecessor-version":[{"id":37168,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32660\/revisions\/37168"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32660"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32660"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}