Department of Electronics, University of Alcalá, Alcalá de Henares, 28801 Madrid, Spain.
Sensors (Basel). 2021 Apr 23;21(9):2958. doi: 10.3390/s21092958.
New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people's movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system.
基于人工智能 (AI) 和深度学习的新处理方法正在取代传统的计算机视觉算法。更先进的系统可以在大型计算设施中处理大量数据。相比之下，本文提出了一种在低功耗嵌入式设备中执行 AI 算法的智能视频监控系统。计算机视觉算法是监控应用的典型算法，旨在检测、计数和跟踪监控区域内人员的运动。此应用程序需要一个分布式智能相机系统。所提出的 AI 应用程序允许使用 MobileNet-SSD 架构在监控区域中检测人员。此外，该算法使用稳健的卡尔曼滤波组，即使在视频中也能跟踪人员，同时还提供人员计数信息。考虑到对过程的限制，检测结果非常出色。边缘节点选择的架构基于 UpSquared2 设备，该设备包括一个能够加速 AI CNN 推断的视觉处理器单元 (VPU)。结果部分提供了有关将多个摄像机连接到同一边缘节点时图像处理时间、人员检测精度和召回率以及系统能耗的信息。结果讨论表明，在分布式监控系统中部署这种智能相机节点非常有用。
