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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| Format: | Book Chapter |
| Language: | English English |
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IOS Press
2017
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42618/ 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 |
| _version_ | 1848826660165517312 |
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| author | Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi |
| author_facet | Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi |
| author_sort | Suryanti, Awang |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:48:21Z |
| format | Book Chapter |
| id | ump-42618 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:48:21Z |
| publishDate | 2017 |
| publisher | IOS Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science QA76 Computer software TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/42618/ http://umpir.ump.edu.my/id/eprint/42618/ http://umpir.ump.edu.my/id/eprint/42618/ 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 |