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.

中文翻译：

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自主物联网相机的近似查询服务

Elf 是能量受限相机的运行时，用于将视频场景持续汇总为近似对象计数。Elf 的新颖之处在于在能量限制下规划相机的计数动作。(1) Elf 探索了由样本图像帧的数量和每帧对象计数器的选择所跨越的丰富的动作空间；它将来自两个来源的错误统一为一个单一的有界错误。(2) 为了在运行时决定计数动作，Elf 采用了基于学习的规划器，在不延迟结果实现的情况下联合优化过去和未来的视频。经过 1,000 多个小时的视频测试，并在现实的能量限制下，Elf 持续生成的对象计数平均仅为真实计数的 11%。除了计数外，Elf 还显示了有限的错误，最多为 3。比竞争基准小​​ 4 倍。在更高的层面上，Elf 为推进视频分析的地理前沿提供了理由。