{"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-learning-for-person-re-identification-a","title":"Deep Learning for Person Re-identification: A Survey and Outlook","arxiv_id":"2001.04193","date":"2020-01-13","proceeding":null,"authors":["Mang Ye","Jianbing Shen","Gaojie Lin","Tao Xiang","Ling Shao","Steven C. H. Hoi"],"abstract":"Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. 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We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for FOUR different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. 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