{"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/deep-relative-distance-learning-tell-the","title":"Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles","arxiv_id":null,"date":"2016-06-01","proceeding":"CVPR 2016 6","authors":["Hongye Liu","Yonghong Tian","Yaowei Yang","Lu Pang","Tiejun Huang"],"abstract":"The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from a large-scale image or video database. However, compared with person re-identification or face recognition, vehicle search problem has long been neglected by researchers in vision community. This paper focuses on an interesting but challenging problem, vehicle re-identification (a.k.a precise vehicle search). We propose a Deep Relative Distance Learning (DRDL) method which exploits a two-branch deep convolutional network to project raw vehicle images into an Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. To further facilitate the future research on this problem, we also present a carefully-organized large-scale image database \"VehicleID\", which includes multiple images of the same vehicle captured by different real-world cameras in a city. We evaluate our DRDL method on our VehicleID dataset and another recently-released vehicle model classification dataset \"CompCars\" in three sets of experiments: vehicle re-identification, vehicle model verification and vehicle retrieval. Experimental results show that our method can achieve promising results and outperforms several state-of-the-art approaches.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2016/html/Liu_Deep_Relative_Distance_CVPR_2016_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2016/papers/Liu_Deep_Relative_Distance_CVPR_2016_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":[],"tasks":[{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"},{"task_slug":"vehicle-re-identification","task_name":"Vehicle Re-Identification"}],"methods":[],"datasets_introduced":[{"slug":"vehicleid","name":"VehicleID","full_name":"PKU VehicleID"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-domain-adaptation-on-veri-776-to-2","task":"Unsupervised Domain Adaptation","dataset":"Veri-776 to VehicleID Large","model":"Mixed Diff + CCL","rank_in_archive_order":13,"of":13,"metrics":{"R-1":"38.20","R-10":"-","R-5":"61.60","mAP":"-"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-veri-776-to-1","task":"Unsupervised Domain Adaptation","dataset":"Veri-776 to VehicleID Medium","model":"Mixed Diff+CCL","rank_in_archive_order":13,"of":13,"metrics":{"R-1":"42.80","R-10":"-","R-5":"66.80","mAP":"-"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-veri-776-to","task":"Unsupervised Domain Adaptation","dataset":"Veri-776 to VehicleID Small","model":"Mixed Diff+CCL","rank_in_archive_order":8,"of":8,"metrics":{" mAP":"-","R-1":"49.00","R-10":"-","R-5":"73.50"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}