摘要：

视频分析通过从视频流中提取高价值信息, 在智能交通和公共安全等应用中发挥着重要作用。传统云端视频分析尽管具备强大的计算能力, 但海量视频数据的上传会带来高带宽占用和网络延迟。边缘计算通过将视频数据下沉至摄像头附近以降低网络延迟, 仍面临着两大挑战: 1)逐帧分析导致重复推理, 而现有帧重用方法无法充分利用历史帧的局部相似性; 2)核心负载不均, 任务在大小核间分配缺乏实时负载感知。为此, 提出一种面向大小核边缘设备的高效视频分析系统Vable。设计多历史帧块级帧重用机制, 将视频帧划分为细粒度块, 并通过树形存储结构与基于局部敏感哈希(LSH)的相似性匹配, 实现跨帧的高效计算结果复用, 从而显著降低冗余推理开销。同时, 提出核心负载感知的列表式有向无环图(DAG)分区算法, 通过实时监测大小核负载状态, 动态分配分析任务, 以平衡计算与通信开销, 避免负载失衡导致延迟增加, 实现Vable的系统原型, 在两个真实数据集上进行了实验评估。实验结果表明, 在UA_V₁中相较于延迟最高的WiseFuse, Vable可将端到端延迟降低59.23%, 在UA_V₂中相较于Faastlane, Vable可将端到端延迟降低45.83%, 在保持高吞吐量的同时有效降低端到端延迟。

关键词: 视频分析, 视频帧重用, 任务调度, 大小核架构, 边缘计算

Abstract:

Video analytics extracts high-value information from video streams and plays a crucial role in applications such as intelligent transportation and public safety. Although traditional cloud-based video analytics offers powerful computational capabilities, uploading massive amounts of video data results in high bandwidth consumption and network latency. Edge computing reduces network latency by processing video data near cameras; however, it still faces two major challenges. First, frame-by-frame analysis leads to redundant inference, and existing frame reuse methods cannot fully exploit local similarities in historical frames. Second, uneven core workload arises because task allocation across big LITTLE lacks real-time load awareness. To address these issues, this study proposes Vable, an efficient video analytics system for big.LITTLE edge devices. It employs a multihistorical frame, block-level frame reuse mechanism, which partitions video frames into fine-grained blocks and employs a tree-based storage structure combined with Locality-Sensitive Hashing (LSH) for similarity matching, enabling efficient cross-frame computation reuse and significantly reduces redundant inference overhead. Additionally, Vable introduces a core workload-aware list-based Directed Acyclic Graph (DAG) partitioning algorithm that dynamically allocates analysis tasks by monitoring the real-time load of big LITTLE, thus balancing computation and communication overhead while avoiding latency increases caused by load imbalance. A prototype of Vable is implemented and evaluated using two real-world datasets. Experimental results demonstrate that Vable reduces the end-to-end latency by 59.23% in UA_V₁ compared to WiseFuse (the highest-latency baseline) and by 45.83% in UA_V₂ compared to Faastlane, effectively lowering the end-to-end latency while maintaining high throughput.

Key words: video analytics, video frame reuse, task scheduling, big.LITTLE architecture, edge computing