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Automatic geotagging and querying of indoor videos

Published:05 November 2013Publication History

ABSTRACT

As mobile videos from smartphones are getting more popular, there have been interests on managing such videos, especially in a large repository. For the management of mobile videos, a new approach has been proposed to organize and search videos using geospatial metadata such as camera location and viewing direction. With the approach, video contents in outdoor space were represented by pure geospatial properties so that well known spatial database technologies can handle video contents more effectively with spatial properties of videos. However, it has been limited by the localization techniques so mainly used for outdoor videos where localization techniques such as GPS is available. Different approaches are required to support geo-tagging of videos in indoor space where no GPS is not available. Due to the recent development of practical indoor localization technique, it becomes possible to tag spatial properties to indoor videos. This work in progress paper introduces a method of automatic geotagging and querying for indoor videos from smartphones.

References

  1. 3dcitydb.org. http://www.3dcitydb.org/.Google ScholarGoogle Scholar
  2. Bimserver.org. http://bimserver.org/.Google ScholarGoogle Scholar
  3. flickr.com. http://www.flickr.com/.Google ScholarGoogle Scholar
  4. Indoorosm wiki. http://wiki.openstreetmap.org/wiki/IndoorOSM/.Google ScholarGoogle Scholar
  5. panoramio.com. http://www.panoramio.com/.Google ScholarGoogle Scholar
  6. S. Arslan Ay, R. Zimmermann, and S. H. Kim. Viewable Scene Modeling for Geospatial Video Search. In 16th ACM Intl. Conference on Multimedia, pages 309--318, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. S. A. Ay, S. H. Kim, and R. Zimmermann. Relevance ranking in georeferenced video search. Multimedia Systems, 16(2):105--125, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. P. Bolliger. Redpin - adaptive, zero-configuration indoor localization through user collaboration. In MELT, pages 55--60, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. K. Kaemarungsi and P. Krishnamurthy. Modeling of indoor positioning systems based on location fingerprinting. In INFOCOM, pages 1012--1022, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  10. S. H. Kim, S. Arslan Ay, and R. Zimmermann. Design and implementation of geo-tagged video search framework. J. Vis. Comun. Image Represent., 21(8):773--786, Nov. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. K.-J. Li and J. Lee. Indoor spatial awareness initiative and standard for indoor spatial data. In Proceedings of IROS 2010, 2010.Google ScholarGoogle Scholar
  12. Z. Shen, S. Arslan Ay, S. H. Kim, and R. Zimmermann. Automatic Tag Generation and Ranking for Sensor-Rich Outdoor Videos. In ACM Intl. Conference on Multimedia, pages 93--102, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Automatic geotagging and querying of indoor videos

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    • Published in

      cover image ACM Conferences
      ISA '13: Proceedings of the Fifth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
      November 2013
      59 pages
      ISBN:9781450325264
      DOI:10.1145/2533810

      Copyright © 2013 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 5 November 2013

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