论文标题
SurveilEdge：基于协作云边缘深度学习的实时视频查询
SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge Deep Learning
论文作者
论文摘要
大规模监视视频数据的实时查询在公共安全和智能运输等各种智能城市应用中起着基本作用。传统的基于云的方法由于较高的传输延迟和超值带宽成本而不适用，而边缘设备通常无法执行由于资源限制而导致的延迟低和准确性高的复杂视觉算法。鉴于仅云和仅边缘解决方案的不可行性，我们介绍了SurveilEdge，这是一种用于大规模监视视频流的实时查询的协作云边缘系统。具体而言，我们设计了卷积神经网络（CNN）培训计划，以高精度减少训练时间，并进行智能任务分配器，以平衡不同计算节点之间的负载，并实现实时查询的延迟 - 准确性交易。我们在具有多个边缘设备和公共云的原型上实施了SurveilEdge，并使用Realworld监视视频数据集进行了广泛的实验。评估结果表明，与仅限云解决方案相比，SurveilEdge的带宽成本最多要低7倍，更快的查询响应时间降低了5.4倍。与仅边缘方法相比，可以提高查询准确性高达43.9％，分别达到15.8倍的速度。
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.
