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Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SFCNNLS) for intra-class variation of vehicle type recognition

Vehicle type recognition has become an important application in Intelligence Transportation Systems (ITSs) to provide a safe and efficient road and transportation infrastructure. There are some challenges in implementing this technology including the complexity of the image that will distract accura...

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Main Authors: Suryanti, Awang, Nik Mohamad Aizuddin, Nik Azmi
Format: Book Chapter
Language:English
English
Published: IOS Press 2017
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/42618/1/Sparse-filtered%20convolutional%20neural%20networks%20with%20layer-skipping.pdf
http://umpir.ump.edu.my/id/eprint/42618/2/Sparse-filtered%20convolutional%20neural%20networks%20with%20layer-skipping%20%28SFCNNLS%29%20for%20intra-class%20variation%20of%20vehicle%20type%20recognition_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42618/
https://doi.org/10.3233/978-1-61499-822-8-194
https://doi.org/10.3233/978-1-61499-822-8-194
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spelling my.ump.umpir.426182024-12-02T01:25:10Z http://umpir.ump.edu.my/id/eprint/42618/ Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SFCNNLS) for intra-class variation of vehicle type recognition Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi QA75 Electronic computers. Computer science QA76 Computer software TA Engineering (General). Civil engineering (General) Vehicle type recognition has become an important application in Intelligence Transportation Systems (ITSs) to provide a safe and efficient road and transportation infrastructure. There are some challenges in implementing this technology including the complexity of the image that will distract accuracy performance, and how to differentiate intra-class variation of the vehicle, for instance, taxi and car. In this paper, we propose to use a deep learning framework that consists of a Sparse-Filtered Convolutional Neural Network with Layer Skipping (SF-CNNLS) strategy to recognize the vehicle type. We implemented 64 sparse filters in Sparse Filtering to extract discriminative features of the vehicle and 2 hidden layers of CNNLS for further processes. The SF-CNNLS can recognize the different types of vehicles due to the combined advantages of each approach. We have evaluated the SF-CNNLS using various classes of vehicle namely car, taxi, and truck. The implementation of the evaluation is during daylight time with different weather conditions and frontal view of the vehicle. From that evaluation, we able to correctly recognize the classes with almost 91% of average accuracy and successfully recognize the taxi as a different class of car. IOS Press 2017-12-01 Book Chapter PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42618/1/Sparse-filtered%20convolutional%20neural%20networks%20with%20layer-skipping.pdf pdf en http://umpir.ump.edu.my/id/eprint/42618/2/Sparse-filtered%20convolutional%20neural%20networks%20with%20layer-skipping%20%28SFCNNLS%29%20for%20intra-class%20variation%20of%20vehicle%20type%20recognition_ABS.pdf Suryanti, Awang and Nik Mohamad Aizuddin, Nik Azmi (2017) Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SFCNNLS) for intra-class variation of vehicle type recognition. In: Deep Learning for Image Processing Applications. IOS Press, Amsterdam, Netherlands, pp. 194-217. ISBN 978-161499822-8, 978-161499821-1 https://doi.org/10.3233/978-1-61499-822-8-194 https://doi.org/10.3233/978-1-61499-822-8-194
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
TA Engineering (General). Civil engineering (General)
Suryanti, Awang
Nik Mohamad Aizuddin, Nik Azmi
Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SFCNNLS) for intra-class variation of vehicle type recognition
description Vehicle type recognition has become an important application in Intelligence Transportation Systems (ITSs) to provide a safe and efficient road and transportation infrastructure. There are some challenges in implementing this technology including the complexity of the image that will distract accuracy performance, and how to differentiate intra-class variation of the vehicle, for instance, taxi and car. In this paper, we propose to use a deep learning framework that consists of a Sparse-Filtered Convolutional Neural Network with Layer Skipping (SF-CNNLS) strategy to recognize the vehicle type. We implemented 64 sparse filters in Sparse Filtering to extract discriminative features of the vehicle and 2 hidden layers of CNNLS for further processes. The SF-CNNLS can recognize the different types of vehicles due to the combined advantages of each approach. We have evaluated the SF-CNNLS using various classes of vehicle namely car, taxi, and truck. The implementation of the evaluation is during daylight time with different weather conditions and frontal view of the vehicle. From that evaluation, we able to correctly recognize the classes with almost 91% of average accuracy and successfully recognize the taxi as a different class of car.
format Book Chapter
author Suryanti, Awang
Nik Mohamad Aizuddin, Nik Azmi
author_facet Suryanti, Awang
Nik Mohamad Aizuddin, Nik Azmi
author_sort Suryanti, Awang
title Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SFCNNLS) for intra-class variation of vehicle type recognition
title_short Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SFCNNLS) for intra-class variation of vehicle type recognition
title_full Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SFCNNLS) for intra-class variation of vehicle type recognition
title_fullStr Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SFCNNLS) for intra-class variation of vehicle type recognition
title_full_unstemmed Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SFCNNLS) for intra-class variation of vehicle type recognition
title_sort sparse-filtered convolutional neural networks with layer-skipping (sfcnnls) for intra-class variation of vehicle type recognition
publisher IOS Press
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/42618/1/Sparse-filtered%20convolutional%20neural%20networks%20with%20layer-skipping.pdf
http://umpir.ump.edu.my/id/eprint/42618/2/Sparse-filtered%20convolutional%20neural%20networks%20with%20layer-skipping%20%28SFCNNLS%29%20for%20intra-class%20variation%20of%20vehicle%20type%20recognition_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42618/
https://doi.org/10.3233/978-1-61499-822-8-194
https://doi.org/10.3233/978-1-61499-822-8-194
_version_ 1822924769053376512
score 13.235362

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