Road enforcement monitoring system based on vehicle type recognition using sparse filtering convolutional neural network with layer skipping strategy (SFCNNLS)

Road Enforcement Monitoring System (REMS) is one of the traffic monitoring systems to monitor the enforcement of a specific route for public transportation in cities. The aim of this system is to automatically and efficiently monitor the enforcement to ensure it is adhered by the traffic users. This...

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Main Authors:	Suryanti, Awang, Nik Mohamad Aizuddin, Nik Azmi, Ngahzaifa, Ab. Ghani
Format:	Conference or Workshop Item
Language:	English English
Published:	Institute of Electrical and Electronics Engineers Inc. (IEEE) 2019
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/24574/ http://umpir.ump.edu.my/id/eprint/24574/ http://umpir.ump.edu.my/id/eprint/24574/1/10.%20Road%20enforcement%20monitoring%20system%20based%20on%20vehicle.pdf http://umpir.ump.edu.my/id/eprint/24574/2/10.1%20Road%20enforcement%20monitoring%20system%20based%20on%20vehicle.pdf

id	ump-24574
recordtype	eprints
spelling	ump-245742019-10-22T04:56:41Z http://umpir.ump.edu.my/id/eprint/24574/ Road enforcement monitoring system based on vehicle type recognition using sparse filtering convolutional neural network with layer skipping strategy (SFCNNLS) Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi Ngahzaifa, Ab. Ghani QA76 Computer software Road Enforcement Monitoring System (REMS) is one of the traffic monitoring systems to monitor the enforcement of a specific route for public transportation in cities. The aim of this system is to automatically and efficiently monitor the enforcement to ensure it is adhered by the traffic users. This aim is difficult to be achieved in current practice that relied on human observation by the authorities. Due to that, we proposed to combine REMS with vehicle type recognition (VTR) method known as Sparse-Filtered Convolutional Neural Network with Layer Skipping-strategy (SF-CNNLS). The purpose of using this method is to recognize and classify the vehicles that use the specific route. It is to prevent any vehicle other than public transportations use that route. The output from VTR will be used by REMS to trigger an immediate message to the authorities for further action. The major challenge of our method is to differentiate taxi and bus as public transportations with car and truck. This is because these vehicles have almost similar features. We tested our method with a self-obtained video that captured from a mounted-camera to observe if the challenge is able to be overcome. For the initial stage, the test is deployed on 4 major vehicle classes; car, taxi, truck and bus. The highest accuracy is obtained from car class with 92.5% and an average accuracy is 81.76%. Based on the test, we proved that our method is able to recognize and classify the vehicle classes although the vehicles are sharing almost similar features. Institute of Electrical and Electronics Engineers Inc. (IEEE) 2019-05 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24574/1/10.%20Road%20enforcement%20monitoring%20system%20based%20on%20vehicle.pdf pdf en http://umpir.ump.edu.my/id/eprint/24574/2/10.1%20Road%20enforcement%20monitoring%20system%20based%20on%20vehicle.pdf Suryanti, Awang and Nik Mohamad Aizuddin, Nik Azmi and Ngahzaifa, Ab. Ghani (2019) Road enforcement monitoring system based on vehicle type recognition using sparse filtering convolutional neural network with layer skipping strategy (SFCNNLS). In: 6th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2019, 12 - 15 April 2018 , Waseda University, Tokyo, Japan. pp. 475-479.. ISBN 978-172810851-3 https://doi.org/10.1109/IEA.2019.8715122
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi Ngahzaifa, Ab. Ghani Road enforcement monitoring system based on vehicle type recognition using sparse filtering convolutional neural network with layer skipping strategy (SFCNNLS)
description	Road Enforcement Monitoring System (REMS) is one of the traffic monitoring systems to monitor the enforcement of a specific route for public transportation in cities. The aim of this system is to automatically and efficiently monitor the enforcement to ensure it is adhered by the traffic users. This aim is difficult to be achieved in current practice that relied on human observation by the authorities. Due to that, we proposed to combine REMS with vehicle type recognition (VTR) method known as Sparse-Filtered Convolutional Neural Network with Layer Skipping-strategy (SF-CNNLS). The purpose of using this method is to recognize and classify the vehicles that use the specific route. It is to prevent any vehicle other than public transportations use that route. The output from VTR will be used by REMS to trigger an immediate message to the authorities for further action. The major challenge of our method is to differentiate taxi and bus as public transportations with car and truck. This is because these vehicles have almost similar features. We tested our method with a self-obtained video that captured from a mounted-camera to observe if the challenge is able to be overcome. For the initial stage, the test is deployed on 4 major vehicle classes; car, taxi, truck and bus. The highest accuracy is obtained from car class with 92.5% and an average accuracy is 81.76%. Based on the test, we proved that our method is able to recognize and classify the vehicle classes although the vehicles are sharing almost similar features.
format	Conference or Workshop Item
author	Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi Ngahzaifa, Ab. Ghani
author_facet	Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi Ngahzaifa, Ab. Ghani
author_sort	Suryanti, Awang
title	Road enforcement monitoring system based on vehicle type recognition using sparse filtering convolutional neural network with layer skipping strategy (SFCNNLS)
title_short	Road enforcement monitoring system based on vehicle type recognition using sparse filtering convolutional neural network with layer skipping strategy (SFCNNLS)
title_full	Road enforcement monitoring system based on vehicle type recognition using sparse filtering convolutional neural network with layer skipping strategy (SFCNNLS)
title_fullStr	Road enforcement monitoring system based on vehicle type recognition using sparse filtering convolutional neural network with layer skipping strategy (SFCNNLS)
title_full_unstemmed	Road enforcement monitoring system based on vehicle type recognition using sparse filtering convolutional neural network with layer skipping strategy (SFCNNLS)
title_sort	road enforcement monitoring system based on vehicle type recognition using sparse filtering convolutional neural network with layer skipping strategy (sfcnnls)
publisher	Institute of Electrical and Electronics Engineers Inc. (IEEE)
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/24574/ http://umpir.ump.edu.my/id/eprint/24574/ http://umpir.ump.edu.my/id/eprint/24574/1/10.%20Road%20enforcement%20monitoring%20system%20based%20on%20vehicle.pdf http://umpir.ump.edu.my/id/eprint/24574/2/10.1%20Road%20enforcement%20monitoring%20system%20based%20on%20vehicle.pdf
first_indexed	2023-09-18T22:37:16Z
last_indexed	2023-09-18T22:37:16Z
_version_	1777416679364493312

Road enforcement monitoring system based on vehicle type recognition using sparse filtering convolutional neural network with layer skipping strategy (SFCNNLS)

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