论文标题
节能停车分析系统使用深入的强化学习
Energy-Efficient Parking Analytics System using Deep Reinforcement Learning
论文作者
论文摘要
智能相机的深视觉技术和无处不在的进步将推动下一代视频分析。但是，视频分析应用程序消耗了大量的能量，因为深度学习技术和相机都是渴望的。在本文中，我们专注于一个停车视频分析平台，并提出了一种基于强化的基于增强学习的技术RL-Camsleep，以启动相机以减少能源足迹，同时保留系统的效用。我们的主要见解是，许多视频分析应用程序并不总是需要运行，并且我们可以设计政策以在必要时才激活视频分析。此外，我们的工作是针对提高硬件和软件效率的现有工作的补充。我们在一个城市规模的停车数据集上评估了我们的方法，该数据集遍布整个城市的76街。我们的分析表明，街道如何具有各种停车方式，强调了适应性政策的重要性。我们的方法可以学习这样的自适应政策，可以将平均能耗降低76.38％，并在执行视频分析时达到98％以上的平均准确度。
Advances in deep vision techniques and ubiquity of smart cameras will drive the next generation of video analytics. However, video analytics applications consume vast amounts of energy as both deep learning techniques and cameras are power-hungry. In this paper, we focus on a parking video analytics platform and propose RL-CamSleep, a deep reinforcement learning-based technique, to actuate the cameras to reduce the energy footprint while retaining the system's utility. Our key insight is that many video-analytics applications do not always need to be operational, and we can design policies to activate video analytics only when necessary. Moreover, our work is complementary to existing work that focuses on improving hardware and software efficiency. We evaluate our approach on a city-scale parking dataset having 76 streets spread across the city. Our analysis demonstrates how streets have various parking patterns, highlighting the importance of an adaptive policy. Our approach can learn such an adaptive policy that can reduce the average energy consumption by 76.38% and achieve an average accuracy of more than 98% in performing video analytics.
