UAV video coverage quality maps and prioritized indexing for wilderness search and rescue

  • Authors:

  • Bryan S. Morse;Cameron H. Engh;Michael A. Goodrich

  • Affiliations:

  • Brigham Young University, Provo, UT, USA;Brigham Young University, Provo, UT, USA;Brigham Young University, Provo, UT, USA

  • Venue:
  • Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
  • Year:
  • 2010

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Abstract

Video-equipped mini unmanned aerial vehicles (mini-UAVs) are becoming increasingly popular for surveillance, remote sensing, law enforcement, and search and rescue operations, all of which rely on thorough coverage of a target observation area. However, coverage is not simply a matter of seeing the area (visibility) but of seeing it well enough to allow detection of targets of interest, a quality we here call "see-ability". Video flashlights, mosaics, or other geospatial compositions of the video may help place the video in context and convey that an area was observed, but not necessarily how well or how often. This paper presents a method for using UAV-acquired video georegistered to terrain and aerial reference imagery to create geospatial video coverage quality maps and indices that indicate relative video quality based on detection factors such as image resolution, number of observations, and variety of viewing angles. When used for offline post-analysis of the video, or for online review, these maps also enable geospatial quality-filtered or prioritized non-sequential access to the video. We present examples of static and dynamic see-ability coverage maps in wilderness search-and-rescue scenarios, along with examples of prioritized non-sequential video access. We also present the results of a user study demonstrating the correlation between see-ability computation and human detection performance.