Real-time video analytics typically require video frames to be processed by a query to identify objects or activities of interest while adhering to an end-to-end frame processing latency constraint. Such applications impose a continuous and heavy load on backend compute and network infrastructure because of the need to stream and process all video frames. Video data has inherent redundancy and does not always contain an object of interest for a given query. We leverage this property of video streams to propose a lightweight Load Shedder that can be deployed on edge servers or on inexpensive edge devices co-located with cameras and drop uninteresting video frames. The proposed Load Shedder uses pixel-level color-based features to calculate a utility score for each ingress video frame, which represents the frame's utility toward the query at hand. The Load Shedder uses a minimum utility threshold to select interesting frames to send for query processing. Dropping unnecessary frames enables the video analytics query in the backend to meet the end-to-end latency constraint with fewer compute and network resources. To guarantee a bounded end-to-end latency at runtime, we introduce a control loop that monitors the backend load for the given query and dynamically adjusts the utility threshold. Performance evaluations show that the proposed Load Shedder selects a large portion of frames containing each object of interest while meeting the end-to-end frame processing latency constraint. Furthermore, the Load Shedder does not impose a significant latency overhead when running on edge devices with modest compute resources.

翻译：实时视频分析通常需要处理视频帧以识别感兴趣的对象或活动，同时满足端到端帧处理延迟约束。由于需要流式传输并处理所有视频帧，这类应用对后端计算与网络基础设施产生了持续且沉重的负载。视频数据具有固有冗余性，且并非每帧都包含特定查询所关注的对象。我们利用视频流的这一特性，提出了一种轻量级负载裁剪器（Load Shedder），可部署于边缘服务器或与摄像头共置的廉价边缘设备上，丢弃不感兴趣的视频帧。该负载裁剪器采用基于像素的颜色特征计算每个输入视频帧的效用分数，用以表征该帧对当前查询的贡献程度。通过设定最小效用阈值，负载裁剪器筛选出感兴趣的帧以提交查询处理。丢弃不必要帧使得后端的视频分析查询能够以更少的计算和网络资源满足端到端延迟约束。为在运行时保证有界的端到端延迟，我们引入了一个控制环路，该环路监控指定查询的后端负载并动态调整效用阈值。性能评估表明，所提负载裁剪器在满足端到端帧处理延迟约束的同时，能够筛选出包含各感兴趣对象的绝大部分帧。此外，在计算资源有限的边缘设备上运行时，该负载裁剪器不会引入显著的延迟开销。