GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

Camera tracking systems have become a common requirement in today’s society. The availability of high quality and inexpensive video cameras and the increasing need for automated video analysis have generated a great deal of interest in numerous fields. Generally, it is not easy to track human behavi...

Full description

Bibliographic Details
Main Authors:	Zalili, Musa, Mohd Zuki, Salleh, Rohani, Abu Bakar, Junzo, Watada
Format:	Article
Language:	English
Published:	IEEE Transactions 2016
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/12725/ http://umpir.ump.edu.my/id/eprint/12725/ http://umpir.ump.edu.my/id/eprint/12725/ http://umpir.ump.edu.my/id/eprint/12725/1/stamp.jsp_tp%3D%26arnumber%3D7108014%26tag%3D1

id	ump-12725
recordtype	eprints
spelling	ump-127252018-09-07T01:47:33Z http://umpir.ump.edu.my/id/eprint/12725/ GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases Zalili, Musa Mohd Zuki, Salleh Rohani, Abu Bakar Junzo, Watada TK Electrical engineering. Electronics Nuclear engineering Camera tracking systems have become a common requirement in today’s society. The availability of high quality and inexpensive video cameras and the increasing need for automated video analysis have generated a great deal of interest in numerous fields. Generally, it is not easy to track human behavior in an environment with a large view. This study aims to address four problems associated with large view in camera tracking system: multiple targets in nonlinear motion, relative size of the targeted object, occlusion and processing time. This paper presents a new method of tracking human movements using a GbLN-PSO and model-based particle filter to address the above problems. The proposed method has been tested with an experimental module using several sets of video data provided by the Eleventh IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) and two other video streams of UBC hockey and Malaysian football games. The experiment has shown that the accuracy of tracking performance has increased up to 25% compared to others reported work in the scientific literature. IEEE Transactions 2016-01-01 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/12725/1/stamp.jsp_tp%3D%26arnumber%3D7108014%26tag%3D1 Zalili, Musa and Mohd Zuki, Salleh and Rohani, Abu Bakar and Junzo, Watada (2016) GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases. IEEE Transactions on Circuits and Systems for Video Technology (99). pp. 1-15. ISSN 1051-8215 http://dx.doi.org/10.1109/TCSVT.2015.2433172 10.1109/TCSVT.2015.2433172
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 Zalili, Musa Mohd Zuki, Salleh Rohani, Abu Bakar Junzo, Watada GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases
description	Camera tracking systems have become a common requirement in today’s society. The availability of high quality and inexpensive video cameras and the increasing need for automated video analysis have generated a great deal of interest in numerous fields. Generally, it is not easy to track human behavior in an environment with a large view. This study aims to address four problems associated with large view in camera tracking system: multiple targets in nonlinear motion, relative size of the targeted object, occlusion and processing time. This paper presents a new method of tracking human movements using a GbLN-PSO and model-based particle filter to address the above problems. The proposed method has been tested with an experimental module using several sets of video data provided by the Eleventh IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS) and two other video streams of UBC hockey and Malaysian football games. The experiment has shown that the accuracy of tracking performance has increased up to 25% compared to others reported work in the scientific literature.
format	Article
author	Zalili, Musa Mohd Zuki, Salleh Rohani, Abu Bakar Junzo, Watada
author_facet	Zalili, Musa Mohd Zuki, Salleh Rohani, Abu Bakar Junzo, Watada
author_sort	Zalili, Musa
title	GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases
title_short	GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases
title_full	GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases
title_fullStr	GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases
title_full_unstemmed	GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases
title_sort	gbln-pso and model-based particle filter approach for tracking human movements in large view cases
publisher	IEEE Transactions
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/12725/ http://umpir.ump.edu.my/id/eprint/12725/ http://umpir.ump.edu.my/id/eprint/12725/ http://umpir.ump.edu.my/id/eprint/12725/1/stamp.jsp_tp%3D%26arnumber%3D7108014%26tag%3D1
first_indexed	2023-09-18T22:14:36Z
last_indexed	2023-09-18T22:14:36Z
_version_	1777415253420670976

GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

Similar Items