  Video-based sensing from aerial drones, especially small multirotor drones,
can provide rich data for numerous applications, including traffic analysis
(computing traffic flow volumes), precision agriculture (periodically
evaluating plant health), and wildlife population management (estimating
population sizes). However, aerial drone video sensing applications must handle
a surprisingly wide range of tasks: video frames must be aligned so that we can
equate coordinates of objects that appear in different frames, video data must
be analyzed to extract application-specific insights, and drone routes must be
computed that maximize the value of newly captured video. To address these
challenges, we built SkyQuery, a novel aerial drone video sensing platform that
provides an expressive, high-level programming language to make it
straightforward for users to develop complex long-running sensing applications.
SkyQuery combines novel methods for fast video frame alignment and detection of
small objects in top-down aerial drone video to efficiently execute
applications with diverse video analysis workflows and data distributions,
thereby allowing application developers to focus on the unique qualities of
their particular application rather than general video processing, data
analysis, and drone routing tasks. We conduct diverse case studies using
SkyQuery in parking monitoring, pedestrian activity mapping, and traffic hazard
detection scenarios to demonstrate the generalizability and effectiveness of
our system.
