Abandoned Baggage Detection & Alert System Via AI and IoT

Z. H. C. Soh, Khadijah Kamarulazizi, K. Daud, I. H. Hamzah, Zuraidi Saad, S. Abdullah
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引用次数: 2

Abstract

Nowadays, piece of unattended luggage there are like tens thousands of lost or left behind in the airports in every year. Due to this issue, this research work aims to develop a camera system that can detect the baggage or luggage and human and notify the authority via media social WhatsApp application when abandoned baggage detected. On top of that, Raspberry Pi 3 model B is used as a hardware where Pi camera is installed in the hardware. This research work is using deep learning method to perform the detection of the baggage and human. The Single Shot Multibox Detector (SSD) is used as the deep learning object detection algorithms for this research work to train the object detection model. The OpenCV and Tensorflow Library is a deep learning library is installed in the Raspberry Pi 3 model B minicomputer to perform the process of detection of the human and baggage. As a result, the Abandoned Baggage Detection and Alert System (ABDAS) able to detected human and baggage using computer vision and the system will notify the authority when the system detected an abandoned baggage through WhatsApp using Twilio Internet of Things (IoT) application software.
通过人工智能和物联网的废弃行李检测和警报系统
如今,每年有成千上万件无人看管的行李丢失或留在机场。鉴于此问题,本研究工作旨在开发一种可以检测行李或行李与人的摄像头系统,并在检测到遗弃行李时通过媒体社交应用WhatsApp通知当局。最重要的是,树莓派3模型B作为硬件,其中Pi相机安装在硬件。本研究工作是利用深度学习的方法对行李和人进行检测。本研究采用单镜头多盒检测器(Single Shot Multibox Detector, SSD)作为深度学习目标检测算法来训练目标检测模型。OpenCV和Tensorflow库是一个深度学习库,安装在Raspberry Pi 3 model B微型计算机上,用于执行对人和行李的检测过程。因此,遗弃行李检测和警报系统(ABDAS)能够使用计算机视觉检测人和行李,当系统使用Twilio物联网(IoT)应用软件通过WhatsApp检测到遗弃行李时,系统将通知当局。
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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