SurveilEdge：基于协同云边缘深度学习的实时的视频查询
SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge Deep Learning
摘要：
海量监控视频数据的实时的【形】查询在公共安全、智能交通等各类智慧城市应用中发挥着基础性作用。传统的基于云的方法由于高传输延迟和高昂的带宽成本而不适用，而边缘设备由于资源有限，通常无法执行具有低延迟和高精度的复杂视觉算法。鉴于仅云和仅边缘解决方案的不可行性，我们提出了SurveilEdge，一个协作的云边缘系统，用于大规模监控视频流的实时的【形】查询。具体而言，我们设计了一个卷积神经网络（CNN）训练方案，以减少训练时间并提高精度;设计了一个智能任务分配器，以平衡不同计算节点之间的负载，实现实时的【形】查询的延迟-精度折衷。我们在具有多个边缘设备和公共云的原型上实现了SurveilEdge，并使用真实世界的监控视频数据集进行了广泛的实验。评估结果表明，SurveilEdge的带宽成本比仅云解决方案低7倍，查询响应时间快5.4倍;与仅边缘方法相比，查询准确率可提高43.9%，速度可提高15.8倍。
英文摘要：
The real-time query of massive surveillance video data plays a fundamental role in various smart urban applications such as public safety and intelligent transportation. Traditional cloud-based approaches are not applicable because of high transmission latency and prohibitive bandwidth cost, while edge devices are often incapable of executing complex vision algorithms with low latency and high accuracy due to restricted resources. Given the infeasibility of both cloud-only and edge-only solutions, we present SurveilEdge, a collaborative cloud-edge system for real-time queries of large-scale surveillance video streams. Specifically, we design a convolutional neural network (CNN) training scheme to reduce the training time with high accuracy, and an intelligent task allocator to balance the load among different computing nodes and to achieve the latency-accuracy tradeoff for real-time queries. We implement SurveilEdge on a prototype with multiple edge devices and a public Cloud, and conduct extensive experiments using realworld surveillance video datasets. Evaluation results demonstrate that SurveilEdge manages to achieve up to 7x less bandwidth cost and 5.4x faster query response time than the cloud-only solution; and can improve query accuracy by up to 43.9% and achieve 15.8x speedup respectively, in comparison with edge-only approaches.
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