Deep learning video analytic systems process live video feeds from multiple cameras with computer vision models deployed on edge or cloud. To optimize utility for these systems, which usually corresponds to query accuracy, efficient bandwidth management for the cameras competing for the fluctuating network resources is crucial. We propose DeepStream, a bandwidth efficient multi-camera video streaming system for deep learning video analytics. DeepStream addresses the challenge of limited and fluctuating bandwidth resources by offering several tailored solutions. We design a novel Regions of Interest detection (ROIDet) algorithm which can run in real time on resource constraint devices, such as Raspberry Pis, to remove spatial redundancy in video frames and reduce the amount of data to be transmitted. We also propose a content-aware bandwidth optimization framework and an Elastic Transmission Mechanism that exploits correlations among video contents. We implement DeepStream on Raspberry Pis and a desktop computer. Evaluations on real-world datasets show that DeepStream's ROIDet algorithm saves up to 54\% bandwidth with less than 1\% accuracy drop. Additionally,DeepStream improves utility by up to 23\% compared to baselines under the same bandwidth conditions.

翻译：深度学习视频分析系统通过部署在边缘或云端的计算机视觉模型处理来自多个摄像头的实时视频流。为优化此类系统的效用（通常对应于查询准确率），对竞争波动网络资源的摄像头进行高效带宽管理至关重要。本文提出DeepStream——一种面向深度学习视频分析的高效带宽多摄像头视频流系统。DeepStream通过提供多种定制化解决方案应对有限且波动的带宽资源挑战。我们设计了一种新颖的感兴趣区域检测算法（ROIDet），该算法可在树莓派等资源受限设备上实时运行，去除视频帧中的空间冗余并减少待传输数据量。同时提出内容感知的带宽优化框架与弹性传输机制，利用视频内容间的相关性。我们在树莓派与台式计算机上实现DeepStream，基于真实数据集的评估表明：DeepStream的ROIDet算法在保持准确率下降低于1%的条件下节省高达54%带宽；在相同带宽条件下，DeepStream相较基线方法提升效用最高达23%。