Real-Time Pose Based Human Detection and Re-Identification with a Single Camera for Robot Person Following

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dc.contributor.advisorBlankenship, Gilmeren_US
dc.contributor.authorWelsh, John Bradforden_US
dc.contributor.departmentElectrical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2017-06-22T06:44:05Z
dc.date.available2017-06-22T06:44:05Z
dc.date.issued2017en_US
dc.description.abstractIn this work we address the challenge of following a person with a mobile robot, with a focus on the image processing aspect. We overview different historical approaches for person following and outline the advantages and disadvantages of each. We then show that recent convolutional neural networks trained for human pose detection are suitable for person detection as it relates to the robot following problem. We extend one such pose detection network to spatially embed the identity of individuals in the image, utilizing the pose features already computed. The proposed identity embedding allows the system to robustly track individuals in consecutive frames even in long term occlusion or absence. The final system provides a robust person tracking scheme which is suitable for person following.en_US
dc.identifierhttps://doi.org/10.13016/M2DW1Q
dc.identifier.urihttp://hdl.handle.net/1903/19566
dc.language.isoenen_US
dc.subject.pqcontrolledElectrical engineeringen_US
dc.subject.pquncontrolledHumanen_US
dc.subject.pquncontrolledIdentificationen_US
dc.subject.pquncontrolledNeural Networken_US
dc.subject.pquncontrolledPoseen_US
dc.subject.pquncontrolledReal-Timeen_US
dc.subject.pquncontrolledRecognitionen_US
dc.titleReal-Time Pose Based Human Detection and Re-Identification with a Single Camera for Robot Person Followingen_US
dc.typeThesisen_US

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