{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108109"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108109","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards accurate person re-identification by deep learning","abstract":"Artificial intelligence surveillance has become increasingly popular due to its security applications. Within this field, person re-identification (re-ID) is a. crucial topic, which aims at matching images of a person in one camera with the images of this person from other cameras. Considering the intensive appearance change of images of the same person, such as lighting, pose and viewpoint, person re-ID is a very challenging problem. In this thesis, we advocate addressing person re-ID by deep learning based methods, which have shown a much better representation ability and much stronger robustness to input data variation and corruption, compared to the traditional approaches. This thesis covers a series of problems involving re-ID including imagebased person re-ID and video-based person re-ID. We start by providing an overview of person re-ID. Following this, we show how to address different reID tasks accurately and efficiently, and our efforts have led to top-performing algorithms on all tasks. The thesis will conclude by describing several promising directions for future research.","abstract_html":"Artificial intelligence surveillance has become increasingly popular due to its security applications. Within this field, person re-identification (re-ID) is a. crucial topic, which aims at matching images of a person in one camera with the images of this person from other cameras. Considering the intensive appearance change of images of the same person, such as lighting, pose and viewpoint, person re-ID is a very challenging problem. In this thesis, we advocate addressing person re-ID by deep learning based methods, which have shown a much better representation ability and much stronger robustness to input data variation and corruption, compared to the traditional approaches. This thesis covers a series of problems involving re-ID including imagebased person re-ID and video-based person re-ID. We start by providing an overview of person re-ID. Following this, we show how to address different reID tasks accurately and efficiently, and our efforts have led to top-performing algorithms on all tasks. The thesis will conclude by describing several promising directions for future research.","abstract_has_math":false,"creators":["Fu, Yang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Huang, Thomas S"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T23:54:32Z","date_published":"2020-08-26T23:54:32Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Person re-ID","Deep Learning"],"languages":["en"],"rights":["Copyright 2020 Yang Fu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108109","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Huang, Thomas S"]},{"key":"dc:creator","label":"Author","values":["Fu, Yang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T23:54:32Z","2022-08-26T23:58:55Z","2020-04-14","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Person re-ID","Deep Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Yang Fu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108109"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Artificial intelligence surveillance has become increasingly popular due to its security applications. Within this field, person re-identification (re-ID) is a. crucial topic, which aims at matching images of a person in one camera with the images of this person from other cameras. Considering the intensive appearance change of images of the same person, such as lighting, pose and viewpoint, person re-ID is a very challenging problem. In this thesis, we advocate addressing person re-ID by deep learning based methods, which have shown a much better representation ability and much stronger robustness to input data variation and corruption, compared to the traditional approaches. This thesis covers a series of problems involving re-ID including imagebased person re-ID and video-based person re-ID. We start by providing an overview of person re-ID. Following this, we show how to address different reID tasks accurately and efficiently, and our efforts have led to top-performing algorithms on all tasks. The thesis will conclude by describing several promising directions for future research.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Yang Fu, accepted the attached license on 2020-04-13 at 21:07.","The student, Yang Fu, submitted this Thesis for approval on 2020-04-13 at 21:14.","This Thesis was approved for publication on 2020-04-14 at 17:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14974 on 2020-08-25 at 17:27:36","Made available in DSpace on 2020-08-26T23:54:32Z (GMT). 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Within this field, person re-identification (re-ID) is a. crucial topic, which aims at matching images of a person in one camera with the images of this person from other cameras. Considering the intensive appearance change of images of the same person, such as lighting, pose and viewpoint, person re-ID is a very challenging problem. In this thesis, we advocate addressing person re-ID by deep learning based methods, which have shown a much better representation ability and much stronger robustness to input data variation and corruption, compared to the traditional approaches. This thesis covers a series of problems involving re-ID including imagebased person re-ID and video-based person re-ID. We start by providing an overview of person re-ID. Following this, we show how to address different reID tasks accurately and efficiently, and our efforts have led to top-performing algorithms on all tasks. The thesis will conclude by describing several promising directions for future research.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Yang Fu, accepted the attached license on 2020-04-13 at 21:07.","The student, Yang Fu, submitted this Thesis for approval on 2020-04-13 at 21:14.","This Thesis was approved for publication on 2020-04-14 at 17:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14974 on 2020-08-25 at 17:27:36","Made available in DSpace on 2020-08-26T23:54:32Z (GMT). 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