A grid-based index and queries for large-scale geo-tagged video collections

  • Authors:

  • He Ma;Sakire Arslan Ay;Roger Zimmermann;Seon Ho Kim

  • Affiliations:

  • School of Computing, National University of Singapore, Singapore, Singapore;School of Computing, National University of Singapore, Singapore, Singapore;School of Computing, National University of Singapore, Singapore, Singapore;Integrated Media Systems Center, University of Southern California, LA, CA

  • Venue:
  • DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
  • Year:
  • 2012

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Abstract

Currently a large number of user-generated videos are produced on a daily basis. It is further increasingly common to combine videos with a variety of meta-data that increase their usefulness. In our prior work we have created a framework for integrated, sensor-rich video acquisition (with one instantiation implemented in the form of smartphone applications) which associates a continuous stream of location and direction information with the acquired videos, hence allowing them to be expressed and manipulated as spatio-temporal objects. In this study we propose a novel multi-level grid-index and a number of related query types that facilitate application access to such augmented, large-scale video repositories. Specifically our grid-index is designed to allow fast access based on a bounded radius and viewing direction --- two criteria that are important in many applications that use videos. We present performance results with a comparison to a multi-dimensional R-tree implementation and show that our approach can provide significant speed improvements of at least 30%, considering a mix of queries.