{"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/vehicle-re-identification-in-aerial-imagery","title":"Vehicle Re-identification in Aerial Imagery: Dataset and Approach","arxiv_id":"1904.01400","date":"2019-04-02","proceeding":"ICCV 2019 10","authors":["Peng Wang","Bingliang Jiao","Lu Yang","Yifei Yang","Shizhou Zhang","Wei Wei","Yanning Zhang"],"abstract":"In this work, we construct a large-scale dataset for vehicle\nre-identification (ReID), which contains 137k images of 13k vehicle instances\ncaptured by UAV-mounted cameras. To our knowledge, it is the largest UAV-based\nvehicle ReID dataset. To increase intra-class variation, each vehicle is\ncaptured by at least two UAVs at different locations, with diverse view-angles\nand flight-altitudes. We manually label a variety of vehicle attributes,\nincluding vehicle type, color, skylight, bumper, spare tire and luggage rack.\nFurthermore, for each vehicle image, the annotator is also required to mark the\ndiscriminative parts that helps them to distinguish this particular vehicle\nfrom others. Besides the dataset, we also design a specific vehicle ReID\nalgorithm to make full use of the rich annotation information. It is capable of\nexplicitly detecting discriminative parts for each specific vehicle and\nsignificantly outperforms the evaluated baselines and state-of-the-art vehicle\nReID approaches.","url_abs":"http://arxiv.org/abs/1904.01400v1","url_pdf":"http://arxiv.org/pdf/1904.01400v1.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":[],"tasks":[{"task_slug":"vehicle-re-identification","task_name":"Vehicle Re-Identification"}],"methods":[],"datasets_introduced":[{"slug":"vrai","name":"VRAI","full_name":"Vehicle Re-identification for Aerial Image"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}