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基于边缘人工智能的具有隐私保护功能的智能监控系统

Intelligent Monitoring System with Privacy Preservation Based on Edge AI.

作者信息

Kim Soohee, Park Joungmin, Jeong Youngwoo, Lee Seung Eun

机构信息

Department of Electronic Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.

出版信息

Micromachines (Basel). 2023 Sep 7;14(9):1749. doi: 10.3390/mi14091749.

Abstract

Currently, the trend of elderly people living alone is rising due to rapid aging and shifts in family structures. Accordingly, the efficient implementation and management of monitoring systems tailored for elderly people living alone have become paramount. Monitoring systems are generally implemented based on multiple sensors, and the collected data are processed on a server to provide monitoring services to users. Due to the use of multiple sensors and a reliance on servers, there are limitations to economical maintenance and a risk of highly personal information being leaked. In this paper, we propose an intelligent monitoring system with privacy preservation based on edge AI. The proposed system achieves cost competitiveness and ensures high security by blocking communication between the camera module and the server with an edge AI module. Additionally, applying edge computing technology allows for the efficient processing of data traffic. The edge AI module was designed with Verilog HDL and was implemented on a field-programmable gate array (FPGA). Through experiments conducted on 6144 frames, we achieved 95.34% accuracy. Synthesis results in a 180 nm CMOS technology indicated a gate count of 1516 K and a power consumption of 344.44 mW.

摘要

目前,由于快速老龄化和家庭结构的变化,老年人独居的趋势正在上升。因此,为独居老年人量身定制的监测系统的有效实施和管理变得至关重要。监测系统通常基于多个传感器来实现,收集到的数据在服务器上进行处理,以向用户提供监测服务。由于使用了多个传感器且依赖服务器,在经济维护方面存在局限性,并且存在高度个人信息泄露的风险。在本文中,我们提出了一种基于边缘人工智能的具有隐私保护功能的智能监测系统。所提出的系统通过边缘人工智能模块阻止相机模块与服务器之间的通信,实现了成本竞争力并确保了高安全性。此外,应用边缘计算技术可以高效处理数据流量。边缘人工智能模块采用Verilog HDL设计,并在现场可编程门阵列(FPGA)上实现。通过对6144帧进行的实验,我们实现了95.34%的准确率。在180纳米CMOS技术下的综合结果表明门数为1516K,功耗为344.44毫瓦。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef27/10536670/0918303087f6/micromachines-14-01749-g001.jpg

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