{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/aerial-ground-person-re-id","title":"Aerial-Ground Person Re-ID","arxiv_id":"2303.08597","date":"2023-03-15","proceeding":null,"authors":["Huy Nguyen","Kien Nguyen","Sridha Sridharan","Clinton Fookes"],"abstract":"Person re-ID matches persons across multiple non-overlapping cameras. Despite the increasing deployment of airborne platforms in surveillance, current existing person re-ID benchmarks' focus is on ground-ground matching and very limited efforts on aerial-aerial matching. We propose a new benchmark dataset - AG-ReID, which performs person re-ID matching in a new setting: across aerial and ground cameras. Our dataset contains 21,983 images of 388 identities and 15 soft attributes for each identity. The data was collected by a UAV flying at altitudes between 15 to 45 meters and a ground-based CCTV camera on a university campus. Our dataset presents a novel elevated-viewpoint challenge for person re-ID due to the significant difference in person appearance across these cameras. We propose an explainable algorithm to guide the person re-ID model's training with soft attributes to address this challenge. Experiments demonstrate the efficacy of our method on the aerial-ground person re-ID task. The dataset will be published and the baseline codes will be open-sourced at https://github.com/huynguyen792/AG-ReID to facilitate research in this area.","url_abs":"https://arxiv.org/abs/2303.08597v5","url_pdf":"https://arxiv.org/pdf/2303.08597v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"aerial-ground-person-re-id","repo_url":"https://github.com/huynguyen792/ag-reid","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"video-based-person-re-identification","task_name":"Video-Based Person Re-Identification"}],"methods":[],"datasets_introduced":[{"slug":"ag-reid","name":"AG-ReID","full_name":"Aerial-Ground Person Re-identification"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/person-re-identification-on-ag-reid","task":"Person Re-Identification","dataset":"AG-ReID","model":"Explain","rank_in_archive_order":2,"of":2,"metrics":{"Averaged rank-1 acc(%)":"81.47"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2303.08597","atlas_url":"https://app.syntology.ai/?focus=2303.08597","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}