A deep learning approach for anomaly detection in large-scale Hajj crowds

Hajj is an annual Islamic event attended by millions of pilgrims every year from around the globe. It is considered to be the biggest religious event that includes large human crowds in the world. Managing such crowds and detecting abnormal behaviors is one of the most significant challenges for the...

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Bibliographic Details
Published in:The Visual computer Vol. 40; no. 8; pp. 5589 - 5603
Main Authors: Aldayri, Amnah, Albattah, Waleed
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024
Springer Nature B.V
Subjects:
Anomalies
Artificial Intelligence
Automation
Behavior
Computer Graphics
Computer Science
Computer vision
Crowd monitoring
Deep learning
Image Processing and Computer Vision
Loss reduction
Original Article
Pilgrimages
Pilgrims
Surveillance
Violations
Surveillance system
Human crowd
Abnormal behavior
Mass gathering
Anomaly detection
ISSN:0178-2789, 1432-2315
Online Access:Get full text
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Summary:Hajj is an annual Islamic event attended by millions of pilgrims every year from around the globe. It is considered to be the biggest religious event that includes large human crowds in the world. Managing such crowds and detecting abnormal behaviors is one of the most significant challenges for the host country, particularly the crowds of pilgrims. Most of the current solutions can only handle small-scale crowd management issues, that involve simple and clear abnormal behaviors. Therefore, there is a need to have a human abnormal behavior detection approach that can deal with large-scale crowd situations. This study aims to propose a computer vision-based framework that automatically analyzes video sequences and detects human abnormal behaviors. The Convolutional LSTM Autoencoder is used for analyzing video scenes and extracting valuable spatial and temporal features. The proposed approach has achieved a good loss reduction of 0.176587 in detecting abnormal pilgrims’ behavior. The results demonstrate a promising picture of the effectiveness of computer vision technologies to detect abnormal behavior in large-scale crowds.
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ISSN:0178-2789
1432-2315
DOI:10.1007/s00371-023-03124-1