vCanteen: A Smart Campus Solution to Elevate University Canteen Experience

2019 IEEE International Smart Cities Conference (ISC2) Pub Date : 2019-10-01 DOI:10.1109/ISC246665.2019.9071672

Boonsita Vatcharakomonphan, Chompakorn Chaksangchaichot, Natnapin Ketchaikosol, Tanapong Tetiranont, Tharit Chullapram, Pattabhum Kosittanakiat, Peeramit Masana, Phirawat Chansajcha, Satsawat Suttawuttiwong, Supakit Thamkittikhun, Samatchaya Wattanachindaporn, Akekamon Boonsith, C. Ratanamahatana, N. Prompoon, M. Pipattanasomporn

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引用次数: 3

Abstract

The persistent problem circulating around various university canteens has always been about high crowd density during lunch hours. To efficiently tackle this issue, a platform called “vCanteen” has been developed that integrates an online food ordering system, a virtual queuing system, together with a machine learning-based crowd estimation system. vCanteen aims at reducing queuing time when ordering food, and allowing users to know the estimated crowd density in a university canteen in real-time. The crowd estimation system has been developed using a multi-column convolutional neural network (MCNN). This paper discusses the vCanteen prototype that was developed and tested at the canteen in the Faculty of Engineering, Chulalongkorn University, Thailand. The description of the crowd estimation system is provided in details including error evaluation and lessons learned.

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vCanteen:提升大学食堂体验的智慧校园解决方案

在各所大学的食堂里，一直存在的问题是午餐时间人群密度过高。为了有效地解决这个问题，一个名为“vCanteen”的平台已经开发出来，该平台集成了在线订餐系统、虚拟排队系统和基于机器学习的人群估计系统。vCanteen旨在减少订餐时的排队时间，并让用户实时了解大学食堂的估计人群密度。利用多列卷积神经网络(MCNN)开发了人群估计系统。本文讨论了在泰国朱拉隆功大学工程学院食堂开发和测试的vCanteen原型。对人群估计系统进行了详细的描述，包括误差评估和经验教训。

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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2019 IEEE International Smart Cities Conference (ISC2)

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