Hajj Crowd Management Using CNN-Based Approach

作     者：Waleed Albattah Muhammad Haris Kaka Khel Shabana Habib Muhammad Islam Sheroz Khan Kushsairy Abdul Kadir 

作者机构：Department of Information TechnologyCollege of ComputerQassim UniversityBuraydahSaudi Arabia Electronic SectionUniversiti Kuala Lumpur British Malaysian InstituteMalaysia Department of Electrical EngineeringOnaizah CollegesSaudi Arabia Department of Electrical and Computer EngineeringInternational Islamic UniversityMalaysia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第66卷第2期

页      面：2183-2197页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the Deputyship for Research&Innovation Ministry of Education in Saudi Arabia for funding this research work through the project number QURDO001 titled“Intelligent Real-time Crowd Monitoring System Using Unmanned Aerial Vehicle(UAV)Video and Global Positioning Systems(GPS)Data” 

主　　题：Crowd management CNN approach Hajj 

摘      要：Hajj as the Muslim holy pilgrimage,attracts millions of humans to Mecca every *** to statists,the pilgrimage has attracted close to 2.5 million pilgrims in 2019,and at its peak,it has attracted over 3 million pilgrims in *** is considered as the world’s largest human *** makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are *** paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during *** image classification system greatly relies on the appropriate dataset used to train the Convolutional neural network(CNN),which is the deep learning technique that has recently attracted the interest of the research community and industry in varying applications of image classification and speech *** core building block of CNN is is a convolutional layer obtained by the getting CNN trained with patches bearing designated features of the trainee *** algorithm is implemented,using the Conv2D layers to activate the CNN as a sequential ***,creating a 2D convolution layer having 64 filters and drop out of 0.5 makes the core of a CNN referred to as a set of *** aim is to train the CNN model with mapped image data,and to make it available for use in classifying the crowd as heavily-crowded,crowded,semi-crowded,light crowded,and *** utility of these results lies in producing appropriate signals for proving helpful in monitoring the *** pilgrims from the photos will help the authorities to determine the number of people in certain *** results demonstrate the utility of agent-based modeling for Hajj pilgrims.