{"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/open-set-person-re-identification","title":"Open-set Person Re-identification","arxiv_id":"1408.0872","date":"2014-08-05","proceeding":null,"authors":["Shengcai Liao","Zhipeng Mo","Jianqing Zhu","Yang Hu","Stan Z. Li"],"abstract":"Person re-identification is becoming a hot research for developing both\nmachine learning algorithms and video surveillance applications. The task of\nperson re-identification is to determine which person in a gallery has the same\nidentity to a probe image. This task basically assumes that the subject of the\nprobe image belongs to the gallery, that is, the gallery contains this person.\nHowever, in practical applications such as searching a suspect in a video, this\nassumption is usually not true. In this paper, we consider the open-set person\nre-identification problem, which includes two sub-tasks, detection and\nidentification. The detection sub-task is to determine the presence of the\nprobe subject in the gallery, and the identification sub-task is to determine\nwhich person in the gallery has the same identity as the accepted probe. We\npresent a database collected from a video surveillance setting of 6 cameras,\nwith 200 persons and 7,413 images segmented. Based on this database, we develop\na benchmark protocol for evaluating the performance under the open-set person\nre-identification scenario. Several popular metric learning algorithms for\nperson re-identification have been evaluated as baselines. From the baseline\nperformance, we observe that the open-set person re-identification problem is\nstill largely unresolved, thus further attention and effort is needed.","url_abs":"http://arxiv.org/abs/1408.0872v2","url_pdf":"http://arxiv.org/pdf/1408.0872v2.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":"metric-learning","task_name":"Metric Learning"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"}],"methods":[],"datasets_introduced":[{"slug":"opereid","name":"OpeReid","full_name":"OpeReid"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}