Deep Learning Based Smart Survilance Robot

V. Ganesan, Smritilekha Das, Tamal Kumar Kundu, Prof. Naren.J, S. Bushra
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引用次数: 1

Abstract

. Surveillance Robot aims to blend IoT capabilities with the support of cloud and machine learning is an advancement to deliver a sophisticated solution for real time security. Industrial and commercial surveillance data security is required for small camera as well as large scale deployment with a drones or robot cars. This paper deals with face recognition using AWS Rekognition and video streaming using AWS kinesis and AWS SNS(Simple notification Service) . AWS Rekognition uses deep learning algorithms to introspect the video stream and find objects / faces on them and compare it with the collection of information that it has trained previously. It detects face with video feed and scans the database to identify the person with AWS Rekognition ,there is also an option of adding new faces by uploading photo of the person to an S3 bucket and face can be indexed . ,
基于深度学习的智能监控机器人
. 监控机器人旨在将物联网功能与云和机器学习的支持相结合,这是一项进步,可以为实时安全提供复杂的解决方案。无论是小型摄像机,还是无人机或机器人汽车的大规模部署,都需要工业和商业监控数据的安全性。本文研究了使用AWS Rekognition的人脸识别和使用AWS kineesis和AWS SNS(Simple notification Service)的视频流。AWS Rekognition使用深度学习算法来内省视频流,并在其中找到对象/面孔,并将其与之前训练过的信息集合进行比较。它通过视频馈送检测人脸,并扫描数据库以使用AWS rekrecognition识别该人,还可以通过将该人的照片上传到S3桶中来添加新面孔,并且可以对面部进行索引。,
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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