Driver behaviour detection using 1D convolutional neural networks
Abstract Driver behaviour is an important factor in road safety. Computer vision techniques have been widely used to monitor the driver behaviour. The violation of privacy and the possibility of spoofing are two continuing challenges in camera‐based systems. To address these challenges, we propose a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-02-01
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| Series: | Electronics Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ell2.12076 |
| _version_ | 1798035964792143872 |
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| author | M. Shahverdy M. Fathy R. Berangi M. Sabokrou |
| author_facet | M. Shahverdy M. Fathy R. Berangi M. Sabokrou |
| author_sort | M. Shahverdy |
| collection | DOAJ |
| description | Abstract Driver behaviour is an important factor in road safety. Computer vision techniques have been widely used to monitor the driver behaviour. The violation of privacy and the possibility of spoofing are two continuing challenges in camera‐based systems. To address these challenges, we propose an efficient approach to monitor and detect driver behaviour based on movement characteristics of the vehicle rather than the visual features of the driver. The main goal of this paper is to classify the driver behaviour into five classes: safe, distracted, aggressive, drunk, and drowsy driving. A lightweight 1D Convolutional Neural Network with high efficiency and low computational complexity is suggested to classify the driver behaviour. Experimental results confirm that our method could successfully classify behaviours of a driver with accuracy of 99.999%. |
| first_indexed | 2024-04-11T21:05:54Z |
| format | Article |
| id | doaj.art-72029c6a0ec5457e90414aecb7b69f15 |
| institution | Directory Open Access Journal |
| issn | 0013-5194 1350-911X |
| language | English |
| last_indexed | 2024-04-11T21:05:54Z |
| publishDate | 2021-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj.art-72029c6a0ec5457e90414aecb7b69f152022-12-22T04:03:21ZengWileyElectronics Letters0013-51941350-911X2021-02-0157311912210.1049/ell2.12076Driver behaviour detection using 1D convolutional neural networksM. Shahverdy0M. Fathy1R. Berangi2M. Sabokrou3Department of Computer Engineering IRAN University of Science and Technology Tehran IranDepartment of Computer Engineering IRAN University of Science and Technology Tehran IranDepartment of Computer Engineering IRAN University of Science and Technology Tehran IranComputer science school Institute for research in fundamental science Tehran IranAbstract Driver behaviour is an important factor in road safety. Computer vision techniques have been widely used to monitor the driver behaviour. The violation of privacy and the possibility of spoofing are two continuing challenges in camera‐based systems. To address these challenges, we propose an efficient approach to monitor and detect driver behaviour based on movement characteristics of the vehicle rather than the visual features of the driver. The main goal of this paper is to classify the driver behaviour into five classes: safe, distracted, aggressive, drunk, and drowsy driving. A lightweight 1D Convolutional Neural Network with high efficiency and low computational complexity is suggested to classify the driver behaviour. Experimental results confirm that our method could successfully classify behaviours of a driver with accuracy of 99.999%.https://doi.org/10.1049/ell2.12076Computer vision and image processing techniquesTraffic engineering computingSocial and behavioural sciences computing |
| spellingShingle | M. Shahverdy M. Fathy R. Berangi M. Sabokrou Driver behaviour detection using 1D convolutional neural networks Electronics Letters Computer vision and image processing techniques Traffic engineering computing Social and behavioural sciences computing |
| title | Driver behaviour detection using 1D convolutional neural networks |
| title_full | Driver behaviour detection using 1D convolutional neural networks |
| title_fullStr | Driver behaviour detection using 1D convolutional neural networks |
| title_full_unstemmed | Driver behaviour detection using 1D convolutional neural networks |
| title_short | Driver behaviour detection using 1D convolutional neural networks |
| title_sort | driver behaviour detection using 1d convolutional neural networks |
| topic | Computer vision and image processing techniques Traffic engineering computing Social and behavioural sciences computing |
| url | https://doi.org/10.1049/ell2.12076 |
| work_keys_str_mv | AT mshahverdy driverbehaviourdetectionusing1dconvolutionalneuralnetworks AT mfathy driverbehaviourdetectionusing1dconvolutionalneuralnetworks AT rberangi driverbehaviourdetectionusing1dconvolutionalneuralnetworks AT msabokrou driverbehaviourdetectionusing1dconvolutionalneuralnetworks |