{"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/phd-learning-learning-with-pompeiu-hausdorff","title":"PhD Learning: Learning With Pompeiu-Hausdorff Distances for Video-Based Vehicle Re-Identification","arxiv_id":null,"date":"2021-06-19","proceeding":"CVPR 2021 1","authors":["Jianan Zhao","Fengliang Qi","Guangyu Ren","Lin Xu"],"abstract":"    Vehicle re-identification (re-ID) is of great significance to urban operation, management, security and has gained more attention in recent years. However, two critical challenges in vehicle re-ID have primarily been underestimated, i.e., 1): how to make full use of raw data, and 2): how to learn a robust re-ID model with noisy data. In this paper, we first create a video vehicle re-ID evaluation benchmark called VVeRI-901 and verify the performance of video-based re-ID is far better than static image-based one. Then we propose a new Pompeiu-hausdorff distance (PhD) learning method for video-to-video matching. It can alleviate the data noise problem caused by the occlusion in videos and thus improve re-ID performance significantly. Extensive empirical results on video-based vehicle and person re-ID datasets, i.e., VVeRI-901, MARS and PRID2011, demonstrate the superiority of the proposed method. The source code of our proposed method is available at https://github.com/emdata-ailab/PhD-Learning.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2021/html/Zhao_PhD_Learning_Learning_With_Pompeiu-Hausdorff_Distances_for_Video-Based_Vehicle_Re-Identification_CVPR_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2021/papers/Zhao_PhD_Learning_Learning_With_Pompeiu-Hausdorff_Distances_for_Video-Based_Vehicle_Re-Identification_CVPR_2021_paper.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":"phd-learning-learning-with-pompeiu-hausdorff","repo_url":"https://github.com/emdata-ailab/PhD-Learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"management","task_name":"Management"},{"task_slug":"vehicle-re-identification","task_name":"Vehicle Re-Identification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}