Deep Person Re-identification Using Supervised Learning with Ranking Method

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dc.contributor.advisorRad, Paul
dc.contributor.advisorHuang, Yufei
dc.contributor.authorMoosavi, Shahla
dc.contributor.committeeMemberLee, Wonjun
dc.date.accessioned2024-02-12T18:29:06Z
dc.date.available2024-02-12T18:29:06Z
dc.date.issued2019
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractIn the present world packed with cameras at every corner the data generated from digital surveillance has become so substantial that it is impossible for human operators to make sense out of. Correspondingly, the intensification of machine vision algorithms that can invest through such data and return consequential perceptions has offered some solutions. Computer Vision techniques such as face detection/recognition and person re-identification has proven their worth into cameras and social medias. Person re-identification is correlating with images of the same person yet taken from different cameras or from the same camera in different incidents. Simply put, allocating a person in multi-camera setting. Us humans, we are easily able to re-identify others by easily descriptors based on the person's appearance (face, height, and build, clothing, hair style, walkingpattern, etc.) but this easy task, is more difficult for a machine to unscramble.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent66 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9781392181225
dc.identifier.urihttps://hdl.handle.net/20.500.12588/4763
dc.languageen
dc.subjectDeep Learning
dc.subjectLearning
dc.subjectPerson
dc.subjectRanking Method
dc.subjectRe-identification
dc.subjectSupervised
dc.subject.classificationComputer engineering
dc.subject.classificationArtificial intelligence
dc.titleDeep Person Re-identification Using Supervised Learning with Ranking Method
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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