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dc.contributor.authorSmolic, Aljosa
dc.contributor.authorChalasani, Tejo
dc.contributor.authorOndrej, Jan
dc.date.accessioned2019-11-13T16:25:12Z
dc.date.available2019-11-13T16:25:12Z
dc.date.issued2018
dc.date.submitted2018en
dc.identifier.citationChalasani, T., Ondrej, J. & Smolic, A., Egocentric Gesture Recognition for Head-Mounted AR devices, Adjunct Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2018, 109-114en
dc.identifier.otherY
dc.identifier.urihttp://hdl.handle.net/2262/90467
dc.descriptionPUBLISHEDen
dc.description.abstractNatural interaction with virtual objects in AR/VR environments makes for a smooth user experience. Gestures are a natural extension from real world to augmented space to achieve these interactions. Finding discriminating spatio-temporal features relevant to gestures and hands in ego-view is the primary challenge for recognising egocentric gestures. In this work we propose a data driven end-toend deep learning approach to address the problem of egocentric gesture recognition, which combines an ego-hand encoder network to find ego-hand features, and a recurrent neural network to discern temporally discriminating features. Since deep learning networks are data intensive, we propose a novel data augmentation technique using green screen capture to alleviate the problem of ground truth annotation. In addition we publish a dataset of 10 gestures performed in a natural fashion in front of a green screen for training and the same 10 gestures performed in different natural scenes without green screen for validation. We also present the results of our network’s performance in comparison to the state-of-the-art using the AirGest dataset.en
dc.description.sponsorshipSFIen
dc.format.extent109-114en
dc.language.isoenen
dc.rightsYen
dc.subjectEgocentric gesture recognitionen
dc.subjectDeep learningen
dc.subjectLSTMsen
dc.subjectHuman computer interfacesen
dc.subjectNatural gesturesen
dc.subjectAugmented realityen
dc.titleEgocentric Gesture Recognition for Head-Mounted AR devicesen
dc.title.alternativeAdjunct Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Forthcoming.en
dc.typeConference Paperen
dc.contributor.sponsorScience Foundation Ireland (SFI)en
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/smolica
dc.identifier.rssinternalid198996
dc.rights.ecaccessrightsopenAccess
dc.contributor.sponsorGrantNumber15/RP/2776en
dc.subject.TCDThemeCreative Technologiesen
dc.subject.TCDTagMultimedia & Creativityen
dc.identifier.rssurihttps://arxiv.org/pdf/1808.05380v1.pdf
dc.status.accessibleNen


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