Cover Image

A review on crowd analysis of evacuation and abnormality detection based on machine learning systems

Human crowds have become hotspot research, particularly in crowd analysis to ensure human safety. Adaptations of machine learning (ML) approaches, especially deep learning, play a vital role in the applications of evacuation, detection, and prediction pertaining to crowd analysis. Further developm...

Full description

Saved in:
Bibliographic Details
Main Authors: Bahamid, Alala, Mohd Ibrahim, Azhar
Format: Article
Language:English
Published: Springer Nature 2022
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/99900/2/NCAA%20alala.pdf
http://irep.iium.edu.my/99900/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.99900
record_format dspace
spelling my.iium.irep.999002022-09-12T00:12:14Z http://irep.iium.edu.my/99900/ A review on crowd analysis of evacuation and abnormality detection based on machine learning systems Bahamid, Alala Mohd Ibrahim, Azhar T Technology (General) Human crowds have become hotspot research, particularly in crowd analysis to ensure human safety. Adaptations of machine learning (ML) approaches, especially deep learning, play a vital role in the applications of evacuation, detection, and prediction pertaining to crowd analysis. Further development in the analysis of crowd is needed to understand human behaviors due to the fast growth of crowd in urban megacities. This article presents a comprehensive review of crowd analysis ML-based systems, where it is categorized with respect to its purposes, viz. crowd evacuation that provides efficient evacuation routes, abnormality detection that could detect the occurrence of any irregular movement or behavior, and crowd prediction that could foresee the occurrence of any possible disasters or predict pedestrian trajectory. Moreover, this article reviews the applied techniques of machine learning with a brief discussion on the used software and simulation platforms. This work also classifies crowd evacuation into data-driven methods and goal-driven learning methods that have attracted significant attention due to their potential to adopt virtual agents with learning capabilities. This review finds that convolutional neural networks and recurrent neural networks have shown superiority in abnormality detection and prediction, whereas deep reinforcement learning has shown potential performance in the development of human level capacities of reasoning. These three methods contribute to the modeling and understanding of pedestrian behavior and will enhance further development in crowd analysis to ensure human safety. Springer Nature 2022-09-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/99900/2/NCAA%20alala.pdf Bahamid, Alala and Mohd Ibrahim, Azhar (2022) A review on crowd analysis of evacuation and abnormality detection based on machine learning systems. Neural Computing and Applications. ISSN 0941-0643 E-ISSN 1433-3058 (In Press) 10.1007/s00521-022-07758-5
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Bahamid, Alala
Mohd Ibrahim, Azhar
A review on crowd analysis of evacuation and abnormality detection based on machine learning systems
description Human crowds have become hotspot research, particularly in crowd analysis to ensure human safety. Adaptations of machine learning (ML) approaches, especially deep learning, play a vital role in the applications of evacuation, detection, and prediction pertaining to crowd analysis. Further development in the analysis of crowd is needed to understand human behaviors due to the fast growth of crowd in urban megacities. This article presents a comprehensive review of crowd analysis ML-based systems, where it is categorized with respect to its purposes, viz. crowd evacuation that provides efficient evacuation routes, abnormality detection that could detect the occurrence of any irregular movement or behavior, and crowd prediction that could foresee the occurrence of any possible disasters or predict pedestrian trajectory. Moreover, this article reviews the applied techniques of machine learning with a brief discussion on the used software and simulation platforms. This work also classifies crowd evacuation into data-driven methods and goal-driven learning methods that have attracted significant attention due to their potential to adopt virtual agents with learning capabilities. This review finds that convolutional neural networks and recurrent neural networks have shown superiority in abnormality detection and prediction, whereas deep reinforcement learning has shown potential performance in the development of human level capacities of reasoning. These three methods contribute to the modeling and understanding of pedestrian behavior and will enhance further development in crowd analysis to ensure human safety.
format Article
author Bahamid, Alala
Mohd Ibrahim, Azhar
author_facet Bahamid, Alala
Mohd Ibrahim, Azhar
author_sort Bahamid, Alala
title A review on crowd analysis of evacuation and abnormality detection based on machine learning systems
title_short A review on crowd analysis of evacuation and abnormality detection based on machine learning systems
title_full A review on crowd analysis of evacuation and abnormality detection based on machine learning systems
title_fullStr A review on crowd analysis of evacuation and abnormality detection based on machine learning systems
title_full_unstemmed A review on crowd analysis of evacuation and abnormality detection based on machine learning systems
title_sort review on crowd analysis of evacuation and abnormality detection based on machine learning systems
publisher Springer Nature
publishDate 2022
url http://irep.iium.edu.my/99900/2/NCAA%20alala.pdf
http://irep.iium.edu.my/99900/
_version_ 1744353532864102400
score 13.211869

Similar Items