Crowd behavior monitoring using self-adaptive social force model

Crowd can be defined as a large number of people gathered closely together. The larger size of crowd results number of behavior either in group or individually. To prevent or minimize the effects of the abnormal behavior, it has to be monitored continuously. Crowd behavior monitoring is an important...

Full description

Bibliographic Details
Main Authors:	Wan Nur Azhani, W. Samsudin, Kamarul Hawari, Ghazali
Format:	Article
Language:	English
Published:	Penerbit UMP 2019
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/24735/ http://umpir.ump.edu.my/id/eprint/24735/ http://umpir.ump.edu.my/id/eprint/24735/ http://umpir.ump.edu.my/id/eprint/24735/7/Crowd%20Behavior%20Monitoring%20using%20Self-Adaptive%20Social%20Force%20Model.pdf

id	ump-24735
recordtype	eprints
spelling	ump-247352019-11-21T05:54:55Z http://umpir.ump.edu.my/id/eprint/24735/ Crowd behavior monitoring using self-adaptive social force model Wan Nur Azhani, W. Samsudin Kamarul Hawari, Ghazali TK Electrical engineering. Electronics Nuclear engineering Crowd can be defined as a large number of people gathered closely together. The larger size of crowd results number of behavior either in group or individually. To prevent or minimize the effects of the abnormal behavior, it has to be monitored continuously. Crowd behavior monitoring is an important task in public places to ensure public safety and avoid any unwanted incidents. It has become a popular research among computer vision communities nowadays due to its needs by the authorities. The current method used is Social Force Model (SFM), which can describe the behavior of a crowd based on the interaction forces between individuals. However, some limitations in the previous works caused by its parameters make it fail to correctly classify the crowd behavior into normal or abnormal. Hence, some modification has been introduced to SFM theory in order to provide significant interaction force; which absolutely portrayed the behavior of the crowd. This work aims to develop a crowd behavior monitoring system using Self-Adaptive SFM. This algorithm is jointly used with Horn-Schunck optical flow as a motion detector for the input video. Instead of using any segmentation methods, the motion of particles in each frame is captured by particle advection method. This is done by advected the particles using the underlying flow vectors of each particle. The obtained new locations for all the particles are necessary in estimating the interaction force of each particle. The combination of psychological and physical parameters in Self-Adaptive SFM makes it more realistic and mimicked the dynamic motion of people in a crowd. The estimated interaction forces of each particle represent the behavior of the crowd, whether it is normal or abnormal. The experimental evaluations on challenging datasets shows that the proposed method achieves the better detection result and outperforms the other methods, optical flow and SFM; with the average accuracy of about 94%. Penerbit UMP 2019 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/24735/7/Crowd%20Behavior%20Monitoring%20using%20Self-Adaptive%20Social%20Force%20Model.pdf Wan Nur Azhani, W. Samsudin and Kamarul Hawari, Ghazali (2019) Crowd behavior monitoring using self-adaptive social force model. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 1 (1). pp. 64-72. ISSN 2637-0883 http://journal.ump.edu.my/mekatronika/article/view/916 https://doi.org/10.15282/mekatronika.v1i1.916
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Wan Nur Azhani, W. Samsudin Kamarul Hawari, Ghazali Crowd behavior monitoring using self-adaptive social force model
description	Crowd can be defined as a large number of people gathered closely together. The larger size of crowd results number of behavior either in group or individually. To prevent or minimize the effects of the abnormal behavior, it has to be monitored continuously. Crowd behavior monitoring is an important task in public places to ensure public safety and avoid any unwanted incidents. It has become a popular research among computer vision communities nowadays due to its needs by the authorities. The current method used is Social Force Model (SFM), which can describe the behavior of a crowd based on the interaction forces between individuals. However, some limitations in the previous works caused by its parameters make it fail to correctly classify the crowd behavior into normal or abnormal. Hence, some modification has been introduced to SFM theory in order to provide significant interaction force; which absolutely portrayed the behavior of the crowd. This work aims to develop a crowd behavior monitoring system using Self-Adaptive SFM. This algorithm is jointly used with Horn-Schunck optical flow as a motion detector for the input video. Instead of using any segmentation methods, the motion of particles in each frame is captured by particle advection method. This is done by advected the particles using the underlying flow vectors of each particle. The obtained new locations for all the particles are necessary in estimating the interaction force of each particle. The combination of psychological and physical parameters in Self-Adaptive SFM makes it more realistic and mimicked the dynamic motion of people in a crowd. The estimated interaction forces of each particle represent the behavior of the crowd, whether it is normal or abnormal. The experimental evaluations on challenging datasets shows that the proposed method achieves the better detection result and outperforms the other methods, optical flow and SFM; with the average accuracy of about 94%.
format	Article
author	Wan Nur Azhani, W. Samsudin Kamarul Hawari, Ghazali
author_facet	Wan Nur Azhani, W. Samsudin Kamarul Hawari, Ghazali
author_sort	Wan Nur Azhani, W. Samsudin
title	Crowd behavior monitoring using self-adaptive social force model
title_short	Crowd behavior monitoring using self-adaptive social force model
title_full	Crowd behavior monitoring using self-adaptive social force model
title_fullStr	Crowd behavior monitoring using self-adaptive social force model
title_full_unstemmed	Crowd behavior monitoring using self-adaptive social force model
title_sort	crowd behavior monitoring using self-adaptive social force model
publisher	Penerbit UMP
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/24735/ http://umpir.ump.edu.my/id/eprint/24735/ http://umpir.ump.edu.my/id/eprint/24735/ http://umpir.ump.edu.my/id/eprint/24735/7/Crowd%20Behavior%20Monitoring%20using%20Self-Adaptive%20Social%20Force%20Model.pdf
first_indexed	2023-09-18T22:37:37Z
last_indexed	2023-09-18T22:37:37Z
_version_	1777416700861349888

Crowd behavior monitoring using self-adaptive social force model

Similar Items