Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/15129
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dc.contributor.authorRao N.
dc.contributor.authorSinha N.
dc.date.accessioned2021-05-05T10:16:30Z-
dc.date.available2021-05-05T10:16:30Z-
dc.date.issued2020
dc.identifier.citationACM International Conference Proceeding Series , Vol. , , p. 411 -en_US
dc.identifier.urihttps://doi.org/10.1145/3430984.3431051
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/15129-
dc.description.abstractWe present an integrated, real-time approach for 2D hand pose detection from a monocular RGB image, with a common backbone shared between the bounding box detector and the keypoint detector subnets. This is in contrast to traditional methods which use two separate models for hand localization and keypoint detection with no sharing of features. We build on the popular RetinaNet architecture for object detection and introduce an integrated model which performs both hand localization and keypoint detection in real-time. We evaluate our approach on two different datasets and show evidence that our model obtains accurate results. © 2021 Owner/Author.en_US
dc.titleAn Integrated Method for Realtime 2D Hand Pose Detectionen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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