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Poster: Pose-assisted active visual recognition in mobile augmented reality

  • Bing Zhou
  • , Sinem Guven
  • , Shu Tao
  • , Fan Ye
  • Stony Brook University
  • IBM

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

While existing visual recognition approaches, which rely on 2D images to train their underlying models, work well for object classification, recognizing the changing state of a 3D object requires addressing several additional challenges. This paper proposes an active visual recognition approach to this problem, leveraging camera pose data available on mobile devices. With this approach, the state of a 3D object, which captures its appearance changes, can be recognized in real time. Our novel approach selects informative video frames filtered by 6-DOF camera poses to train a deep learning model to recognize object state.We validate our approach through a prototype for Augmented Reality-assisted hardware maintenance.

Original languageEnglish
Title of host publicationMobiCom 2018 - Proceedings of the 24th Annual International Conference on Mobile Computing and Networking
PublisherAssociation for Computing Machinery
Pages756-758
Number of pages3
ISBN (Electronic)9781450359030
DOIs
StatePublished - Oct 15 2018
Event24th Annual International Conference on Mobile Computing and Networking, MobiCom 2018 - New Delhi, India
Duration: Oct 29 2018Nov 2 2018

Publication series

NameProceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM

Conference

Conference24th Annual International Conference on Mobile Computing and Networking, MobiCom 2018
Country/TerritoryIndia
CityNew Delhi
Period10/29/1811/2/18

Keywords

  • active visual recognition
  • augmented reality
  • mobile devices

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