CROWD MONITORING AND CONTROLLING SYSTEM FOR HOMELAND SECURITY IMPROVEMENT

In an era where security concerns are paramount, developing advanced technological solutions is crucial to enhance public safety in crowded environments. This study introduces an innovative Internet of Things (IoT) based device designed to detect facial emotions and identify weapons among individual...

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Main Authors: Ahmad Abubakar Mustapha, Mohamed Sirajudeen Yoosuf
Format: Article
Language:English
Published: University of Kragujevac 2025-03-01
Series:Proceedings on Engineering Sciences
Subjects:
Online Access:https://pesjournal.net/journal/v7-n1/51.pdf
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author Ahmad Abubakar Mustapha
Mohamed Sirajudeen Yoosuf
author_facet Ahmad Abubakar Mustapha
Mohamed Sirajudeen Yoosuf
author_sort Ahmad Abubakar Mustapha
collection DOAJ
description In an era where security concerns are paramount, developing advanced technological solutions is crucial to enhance public safety in crowded environments. This study introduces an innovative Internet of Things (IoT) based device designed to detect facial emotions and identify weapons among individuals in real-time within crowded spaces. Our Proposed work is through the integration of IoT and machine learning algorithms, the device can detect and analyze facial emotions and weapons in crowded areas (among protesters), the model has been experimented and it achieved an mAP of 99.39% @0.50IOU while 98.69% @0.75IOU and overall mAP of 91.52%. This device offers a proactive approach to security monitoring, enabling swift responses to predict weapons and emotions in a crowd's suspicious activities and enhancing overall situational awareness.
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publishDate 2025-03-01
publisher University of Kragujevac
record_format Article
series Proceedings on Engineering Sciences
spelling doaj-art-be1ea3317d7a4246a96c2036fffb2dba2025-08-20T02:57:21ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112025-03-017148549410.24874/PES07.01D.005CROWD MONITORING AND CONTROLLING SYSTEM FOR HOMELAND SECURITY IMPROVEMENTAhmad Abubakar Mustapha 0https://orcid.org/0000-0001-6872-4097Mohamed Sirajudeen Yoosuf 1https://orcid.org/0000-0002-4640-4815VIT-AP University, School of Computer Science and Engineering, Amaravati, Andhra Pradesh, India, India VIT-AP University, School of Computer Science and Engineering, Amaravati, Andhra Pradesh, India, India In an era where security concerns are paramount, developing advanced technological solutions is crucial to enhance public safety in crowded environments. This study introduces an innovative Internet of Things (IoT) based device designed to detect facial emotions and identify weapons among individuals in real-time within crowded spaces. Our Proposed work is through the integration of IoT and machine learning algorithms, the device can detect and analyze facial emotions and weapons in crowded areas (among protesters), the model has been experimented and it achieved an mAP of 99.39% @0.50IOU while 98.69% @0.75IOU and overall mAP of 91.52%. This device offers a proactive approach to security monitoring, enabling swift responses to predict weapons and emotions in a crowd's suspicious activities and enhancing overall situational awareness.https://pesjournal.net/journal/v7-n1/51.pdfcrowd monitoringobject detectionhomeland securitycomputer visioniot
spellingShingle Ahmad Abubakar Mustapha
Mohamed Sirajudeen Yoosuf
CROWD MONITORING AND CONTROLLING SYSTEM FOR HOMELAND SECURITY IMPROVEMENT
Proceedings on Engineering Sciences
crowd monitoring
object detection
homeland security
computer vision
iot
title CROWD MONITORING AND CONTROLLING SYSTEM FOR HOMELAND SECURITY IMPROVEMENT
title_full CROWD MONITORING AND CONTROLLING SYSTEM FOR HOMELAND SECURITY IMPROVEMENT
title_fullStr CROWD MONITORING AND CONTROLLING SYSTEM FOR HOMELAND SECURITY IMPROVEMENT
title_full_unstemmed CROWD MONITORING AND CONTROLLING SYSTEM FOR HOMELAND SECURITY IMPROVEMENT
title_short CROWD MONITORING AND CONTROLLING SYSTEM FOR HOMELAND SECURITY IMPROVEMENT
title_sort crowd monitoring and controlling system for homeland security improvement
topic crowd monitoring
object detection
homeland security
computer vision
iot
url https://pesjournal.net/journal/v7-n1/51.pdf
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