  Elf is a runtime for an energy-constrained camera to continuously summarize
video scenes as approximate object counts. Elf's novelty centers on planning
the camera's count actions under energy constraint. (1) Elf explores the rich
action space spanned by the number of sample image frames and the choice of
per-frame object counters; it unifies errors from both sources into one single
bounded error. (2) To decide count actions at run time, Elf employs a
learning-based planner, jointly optimizing for past and future videos without
delaying result materialization. Tested with more than 1,000 hours of videos
and under realistic energy constraints, Elf continuously generates object
counts within only 11% of the true counts on average. Alongside the counts, Elf
presents narrow errors shown to be bounded and up to 3.4x smaller than
competitive baselines. At a higher level, Elf makes a case for advancing the
geographic frontier of video analytics.
