GeoVideoIndex: Indexing for georeferenced videos

Dongha Lee, Jinoh Oh, Woong Kee Loh, Hwanjo Yu

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Videos captured with spatiotemporal information such as time, location, and camera direction are called georeferenced videos. As recent video recording devices such as smartphones, action camcorders, and dashcams have built-in GPS sensors, they capture videos with spatiotemporal information, and such spatiotemporal information can be used for querying georeferenced videos. For a video search system supporting location queries, an efficient spatial index is important to find the query results fast while maintaining its size small. This paper proposes an efficient indexing method for searching georeferenced videos, called GeoVideoIndex. GeoVideoIndex adopts MBTR(Minimum Bounding Tilted Rectangle) in leaf nodes, as an MBTR can efficiently represent the viewable areas of a camera along the trajectory. GeoVideoIndex constructs MBTRs only based on the linear change of camera moving direction, in order to form a long MBTR covering a linear piece of the trajectory. In particular, GeoVideoIndex applies a data compression technique, which excludes superfluous scenes and stores data in a compact form. We experimentally compared the performance of spatial indexing methods on both real and synthetic datasets, and GeoVideoIndex substantially reduces the index size and the construction time. GeoVideoIndex also processes location queries much faster than other methods as well as manages vast amount of scenes compactly.

Original languageEnglish
Pages (from-to)210-223
Number of pages14
JournalInformation sciences
Publication statusPublished - 2016 Dec 20

Bibliographical note

Publisher Copyright:
© 2016 Elsevier Inc.

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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