An expert crowd monitoring and management framework for Hajj

Anan, Muhammad; Awad, Mohammad Faiz Chikh; Bin-Abbas, Hesham; Karim, Lutful

doi:10.1109/wincom.2017.8238202

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…The deep learning frameworks like CNN and Long Short Term Memory (LSTM) have been used in [58] for crowd density estimation. In [38] an expert crowd control and management system for hajj has been used with three strategies i.e., to address congestion and overcrowded situation using; First In First Out (FIFO), priority queuing and Weighted Round Robin (WRR). An automatic multiple human detection method using hybrid adaptive Gaussian mixture model was introduced in [82] for human detection.…”

Section: Discussionmentioning

confidence: 99%

“…The model can be used to monitor and manage crowd in Mataf (place of Tawaf) in real time. Similarly in [38], the authors have proposed a framework weighted round robin to overcome the congestion and overcrowded during hajj (pilgrimage). The framework was designed to be proactive in accurately predicting potential problems through the use of smart monitoring of each path of rituals locations.…”

Section: Crowd Managementmentioning

confidence: 99%

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Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

et al. 2020

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Crowd management and monitoring is crucial for maintaining public safety and is an important research topic. Developing a robust crowd monitoring system (CMS) is a challenging task as it involves addressing many key issues such as density variation, irregular distribution of objects, occlusions, pose estimation, etc. Crowd gathering at various places like hospitals, parks, stadiums, airports, cultural and religious points are usually monitored by Close Circuit Television (CCTV) cameras. The drawbacks of CCTV cameras are: limited area coverage, installation problems, movability, high power consumption and constant monitoring by the operators. Therefore, many researchers have turned towards computer vision and machine learning that have overcome these issues by minimizing the need of human involvement. This review is aimed to categorize, analyze as well as provide the latest development and performance evolution in crowd monitoring using different machine learning techniques and methods that are published in journals and conferences over the past five years.

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Section: Discussionmentioning

confidence: 99%

Section: Crowd Managementmentioning

confidence: 99%

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

et al. 2020

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“…The interface layer of the proposed framework captures users' sensory data from their mobile devices, followed by a management layer, which extracts the information from the collected data and serves users with vital information about open roads and passages, locating non-crowded areas and finding their groups and friends. An expert crowd monitoring and management IoT-based framework was presented by Nasser et al [36] which is designed to predict possible problems by monitoring the paths leading to the location of the rituals.…”

Section: B Wireless Technologymentioning

confidence: 99%

“…Nasser et al, [36] utilized RFID and WLAN technologies and proposed an expert crowd management framework that integrated crowd density information with available mobility paths to facilitate the efficient movement of traffic within Mashaer 11 . Furthermore, the framework was proactive in predicting potential problems related to ritual location paths, followed by a concept to substitute the current human force system of crowd control and management.…”

Section: B1 Wi-fimentioning

confidence: 99%

Digital Revolution for Hajj Crowd Management: A Technology Survey

et al. 2020

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“…The proposed method is based on permutation, diffusion, and pixel randomization process, at first the proposed method generates chaotic two sequences by the using of the quadratic and the cubic map, then the generated two sequences will be used to shuffle the plain image. the shuffled image will decomposed into its bit-plain components to be encrypted later by the using of confusion and diffusion process [13]. Gu et al…”

Section: Introductionmentioning

confidence: 99%

Ultra-key Space Domain for Image Encryption using Chaos-based Approach with DNA Sequence

Albidewi¹,

Alromema²

2021

IJACSA

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Recently, image encryption has taken an importance especially after the dramatic evolution of the internet and network communication. The importance of securing the images contents is due to the simplicity of capturing and transferring of digital images in various communication media. Although there are many approaches for image encryptions, chaos-based image encryption approach is considered one of the most appropriate approaches because of its simplicity, security, and sensitivity to the input parameter. This research paper presents a new technique for encrypting RGB image components using nonlinear chaotic function and DNA sequence. A new image with the same dimensions of the plain-image is used as a key for confusions and diffusion process for each RGB components of plain-image. Experimental results show the efficiency of the proposed technique, simplicity, and high level of resistant against several cryptanalyst.

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An expert crowd monitoring and management framework for Hajj

Cited by 15 publications

References 14 publications

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

Digital Revolution for Hajj Crowd Management: A Technology Survey

Ultra-key Space Domain for Image Encryption using Chaos-based Approach with DNA Sequence

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