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MARVEL: Enabling Mobile Augmented Reality with Low Energy and Low Latency

DC Field Value Language
dc.contributor.authorChen, Kaifei-
dc.contributor.authorLi, Tong-
dc.contributor.authorKim, Hyung-Sin-
dc.contributor.authorCuller, David E.-
dc.contributor.authorKatz, Randy H.-
dc.date.accessioned2024-05-17T07:37:05Z-
dc.date.available2024-05-17T07:37:05Z-
dc.date.created2024-05-17-
dc.date.issued2018-
dc.identifier.citationSENSYS'18: PROCEEDINGS OF THE 16TH CONFERENCE ON EMBEDDED NETWORKED SENSOR SYSTEMS, pp.292-304-
dc.identifier.urihttps://hdl.handle.net/10371/203161-
dc.description.abstractThis paper presents MARVEL, a mobile augmented reality (MAR) system which provides a notation display service with imperceptible latency (< 100 ms) and low energy consumption on regular mobile devices. In contrast to conventional MAR systems, which recognize objects using image-based computations performed in the cloud, MARVEL mainly utilizes a mobile device's local inertial sensors for recognizing and tracking multiple objects, while computing local optical flow and offloading images only when necessary. We propose a system architecture which uses local inertial tracking, local optical flow, and visual tracking in the cloud synergistically. On top of that, we investigate how to minimize the overhead for image computation and offloading. We have implemented and deployed a holistic prototype system in a commercial building and evaluate MARVEL's performance. The efficient use of a mobile device's capabilities lowers latency and energy consumption without sacrificing accuracy.-
dc.language영어-
dc.publisherASSOC COMPUTING MACHINERY-
dc.titleMARVEL: Enabling Mobile Augmented Reality with Low Energy and Low Latency-
dc.typeArticle-
dc.identifier.doi10.1145/3274783.3274834-
dc.citation.journaltitleSENSYS'18: PROCEEDINGS OF THE 16TH CONFERENCE ON EMBEDDED NETWORKED SENSOR SYSTEMS-
dc.identifier.wosid000469016400023-
dc.identifier.scopusid2-s2.0-85061701965-
dc.citation.endpage304-
dc.citation.startpage292-
dc.description.isOpenAccessY-
dc.contributor.affiliatedAuthorKim, Hyung-Sin-
dc.type.docTypeProceedings Paper-
dc.description.journalClass1-
dc.subject.keywordAuthorMobile Augmented Reality-
dc.subject.keywordAuthorSelective Offloading-
dc.subject.keywordAuthorVisual-Inertial SLAM-
dc.subject.keywordAuthorMobile Computing-
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Research Area Distributed machine learning, Edge, Mobile AI

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