Hands on Wheel Classification Based on Depth Images and Neural Networks

This paper describes a system to automatically observe if the driver has his hands on the wheel, which is important to know that he can intervene if necessary. To accomplish this an artificial neural network is used, which utilizes depth information captured by a camera in the roof module of the car...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Schmitz Jan-Christoph [verfasserIn] Tilgner Stephan [verfasserIn] Kalischewski Kathrin [verfasserIn] Wagner Daniel [verfasserIn] Kummert Anton [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch ; Französisch

Erschienen:	2020

Übergeordnetes Werk:	In: MATEC Web of Conferences - EDP Sciences, 2013, 308, p 06003(2020)
Übergeordnetes Werk:	volume:308, p 06003 ; year:2020

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1051/matecconf/202030806003

Katalog-ID:	DOAJ054652065

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allfields	10.1051/matecconf/202030806003 doi (DE-627)DOAJ054652065 (DE-599)DOAJafa92e39474547279e3c30487ce12a53 DE-627 ger DE-627 rakwb eng fre TA1-2040 Schmitz Jan-Christoph verfasserin aut Hands on Wheel Classification Based on Depth Images and Neural Networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper describes a system to automatically observe if the driver has his hands on the wheel, which is important to know that he can intervene if necessary. To accomplish this an artificial neural network is used, which utilizes depth information captured by a camera in the roof module of the car. This means that the driver and the steering wheel are viewed from above. The created classification system is described. It is designed to require as little computational effort as possible, since the target application is on an embedded system in the car. A dataset is presented and the effect of a class imbalance that is incorporated in it is studied. Furthermore, it is examined which part, i.e. the depth or the intensity image, of the available data is important to achieve the best possible performance. Finally, by examining a learning curve, an experiment is made to find out whether the recording of further training data would be reasonable. Engineering (General). Civil engineering (General) Tilgner Stephan verfasserin aut Kalischewski Kathrin verfasserin aut Wagner Daniel verfasserin aut Kummert Anton verfasserin aut In MATEC Web of Conferences EDP Sciences, 2013 308, p 06003(2020) (DE-627)720166209 (DE-600)2673602-0 2261236X nnns volume:308, p 06003 year:2020 https://doi.org/10.1051/matecconf/202030806003 kostenfrei https://doaj.org/article/afa92e39474547279e3c30487ce12a53 kostenfrei https://www.matec-conferences.org/articles/matecconf/pdf/2020/04/matecconf_ictte2019_06003.pdf kostenfrei https://doaj.org/toc/2261-236X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 308, p 06003 2020
spelling	10.1051/matecconf/202030806003 doi (DE-627)DOAJ054652065 (DE-599)DOAJafa92e39474547279e3c30487ce12a53 DE-627 ger DE-627 rakwb eng fre TA1-2040 Schmitz Jan-Christoph verfasserin aut Hands on Wheel Classification Based on Depth Images and Neural Networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper describes a system to automatically observe if the driver has his hands on the wheel, which is important to know that he can intervene if necessary. To accomplish this an artificial neural network is used, which utilizes depth information captured by a camera in the roof module of the car. This means that the driver and the steering wheel are viewed from above. The created classification system is described. It is designed to require as little computational effort as possible, since the target application is on an embedded system in the car. A dataset is presented and the effect of a class imbalance that is incorporated in it is studied. Furthermore, it is examined which part, i.e. the depth or the intensity image, of the available data is important to achieve the best possible performance. Finally, by examining a learning curve, an experiment is made to find out whether the recording of further training data would be reasonable. Engineering (General). Civil engineering (General) Tilgner Stephan verfasserin aut Kalischewski Kathrin verfasserin aut Wagner Daniel verfasserin aut Kummert Anton verfasserin aut In MATEC Web of Conferences EDP Sciences, 2013 308, p 06003(2020) (DE-627)720166209 (DE-600)2673602-0 2261236X nnns volume:308, p 06003 year:2020 https://doi.org/10.1051/matecconf/202030806003 kostenfrei https://doaj.org/article/afa92e39474547279e3c30487ce12a53 kostenfrei https://www.matec-conferences.org/articles/matecconf/pdf/2020/04/matecconf_ictte2019_06003.pdf kostenfrei https://doaj.org/toc/2261-236X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 308, p 06003 2020
allfields_unstemmed	10.1051/matecconf/202030806003 doi (DE-627)DOAJ054652065 (DE-599)DOAJafa92e39474547279e3c30487ce12a53 DE-627 ger DE-627 rakwb eng fre TA1-2040 Schmitz Jan-Christoph verfasserin aut Hands on Wheel Classification Based on Depth Images and Neural Networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper describes a system to automatically observe if the driver has his hands on the wheel, which is important to know that he can intervene if necessary. To accomplish this an artificial neural network is used, which utilizes depth information captured by a camera in the roof module of the car. This means that the driver and the steering wheel are viewed from above. The created classification system is described. It is designed to require as little computational effort as possible, since the target application is on an embedded system in the car. A dataset is presented and the effect of a class imbalance that is incorporated in it is studied. Furthermore, it is examined which part, i.e. the depth or the intensity image, of the available data is important to achieve the best possible performance. Finally, by examining a learning curve, an experiment is made to find out whether the recording of further training data would be reasonable. Engineering (General). Civil engineering (General) Tilgner Stephan verfasserin aut Kalischewski Kathrin verfasserin aut Wagner Daniel verfasserin aut Kummert Anton verfasserin aut In MATEC Web of Conferences EDP Sciences, 2013 308, p 06003(2020) (DE-627)720166209 (DE-600)2673602-0 2261236X nnns volume:308, p 06003 year:2020 https://doi.org/10.1051/matecconf/202030806003 kostenfrei https://doaj.org/article/afa92e39474547279e3c30487ce12a53 kostenfrei https://www.matec-conferences.org/articles/matecconf/pdf/2020/04/matecconf_ictte2019_06003.pdf kostenfrei https://doaj.org/toc/2261-236X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 308, p 06003 2020
allfieldsGer	10.1051/matecconf/202030806003 doi (DE-627)DOAJ054652065 (DE-599)DOAJafa92e39474547279e3c30487ce12a53 DE-627 ger DE-627 rakwb eng fre TA1-2040 Schmitz Jan-Christoph verfasserin aut Hands on Wheel Classification Based on Depth Images and Neural Networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper describes a system to automatically observe if the driver has his hands on the wheel, which is important to know that he can intervene if necessary. To accomplish this an artificial neural network is used, which utilizes depth information captured by a camera in the roof module of the car. This means that the driver and the steering wheel are viewed from above. The created classification system is described. It is designed to require as little computational effort as possible, since the target application is on an embedded system in the car. A dataset is presented and the effect of a class imbalance that is incorporated in it is studied. Furthermore, it is examined which part, i.e. the depth or the intensity image, of the available data is important to achieve the best possible performance. Finally, by examining a learning curve, an experiment is made to find out whether the recording of further training data would be reasonable. Engineering (General). Civil engineering (General) Tilgner Stephan verfasserin aut Kalischewski Kathrin verfasserin aut Wagner Daniel verfasserin aut Kummert Anton verfasserin aut In MATEC Web of Conferences EDP Sciences, 2013 308, p 06003(2020) (DE-627)720166209 (DE-600)2673602-0 2261236X nnns volume:308, p 06003 year:2020 https://doi.org/10.1051/matecconf/202030806003 kostenfrei https://doaj.org/article/afa92e39474547279e3c30487ce12a53 kostenfrei https://www.matec-conferences.org/articles/matecconf/pdf/2020/04/matecconf_ictte2019_06003.pdf kostenfrei https://doaj.org/toc/2261-236X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 308, p 06003 2020
allfieldsSound	10.1051/matecconf/202030806003 doi (DE-627)DOAJ054652065 (DE-599)DOAJafa92e39474547279e3c30487ce12a53 DE-627 ger DE-627 rakwb eng fre TA1-2040 Schmitz Jan-Christoph verfasserin aut Hands on Wheel Classification Based on Depth Images and Neural Networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper describes a system to automatically observe if the driver has his hands on the wheel, which is important to know that he can intervene if necessary. To accomplish this an artificial neural network is used, which utilizes depth information captured by a camera in the roof module of the car. This means that the driver and the steering wheel are viewed from above. The created classification system is described. It is designed to require as little computational effort as possible, since the target application is on an embedded system in the car. A dataset is presented and the effect of a class imbalance that is incorporated in it is studied. Furthermore, it is examined which part, i.e. the depth or the intensity image, of the available data is important to achieve the best possible performance. Finally, by examining a learning curve, an experiment is made to find out whether the recording of further training data would be reasonable. Engineering (General). Civil engineering (General) Tilgner Stephan verfasserin aut Kalischewski Kathrin verfasserin aut Wagner Daniel verfasserin aut Kummert Anton verfasserin aut In MATEC Web of Conferences EDP Sciences, 2013 308, p 06003(2020) (DE-627)720166209 (DE-600)2673602-0 2261236X nnns volume:308, p 06003 year:2020 https://doi.org/10.1051/matecconf/202030806003 kostenfrei https://doaj.org/article/afa92e39474547279e3c30487ce12a53 kostenfrei https://www.matec-conferences.org/articles/matecconf/pdf/2020/04/matecconf_ictte2019_06003.pdf kostenfrei https://doaj.org/toc/2261-236X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 308, p 06003 2020
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source	In MATEC Web of Conferences 308, p 06003(2020) volume:308, p 06003 year:2020
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callnumber-first	T - Technology
author	Schmitz Jan-Christoph
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topic_title	TA1-2040 Hands on Wheel Classification Based on Depth Images and Neural Networks
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title	Hands on Wheel Classification Based on Depth Images and Neural Networks
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title_auth	Hands on Wheel Classification Based on Depth Images and Neural Networks
abstract	This paper describes a system to automatically observe if the driver has his hands on the wheel, which is important to know that he can intervene if necessary. To accomplish this an artificial neural network is used, which utilizes depth information captured by a camera in the roof module of the car. This means that the driver and the steering wheel are viewed from above. The created classification system is described. It is designed to require as little computational effort as possible, since the target application is on an embedded system in the car. A dataset is presented and the effect of a class imbalance that is incorporated in it is studied. Furthermore, it is examined which part, i.e. the depth or the intensity image, of the available data is important to achieve the best possible performance. Finally, by examining a learning curve, an experiment is made to find out whether the recording of further training data would be reasonable.
abstractGer	This paper describes a system to automatically observe if the driver has his hands on the wheel, which is important to know that he can intervene if necessary. To accomplish this an artificial neural network is used, which utilizes depth information captured by a camera in the roof module of the car. This means that the driver and the steering wheel are viewed from above. The created classification system is described. It is designed to require as little computational effort as possible, since the target application is on an embedded system in the car. A dataset is presented and the effect of a class imbalance that is incorporated in it is studied. Furthermore, it is examined which part, i.e. the depth or the intensity image, of the available data is important to achieve the best possible performance. Finally, by examining a learning curve, an experiment is made to find out whether the recording of further training data would be reasonable.
abstract_unstemmed	This paper describes a system to automatically observe if the driver has his hands on the wheel, which is important to know that he can intervene if necessary. To accomplish this an artificial neural network is used, which utilizes depth information captured by a camera in the roof module of the car. This means that the driver and the steering wheel are viewed from above. The created classification system is described. It is designed to require as little computational effort as possible, since the target application is on an embedded system in the car. A dataset is presented and the effect of a class imbalance that is incorporated in it is studied. Furthermore, it is examined which part, i.e. the depth or the intensity image, of the available data is important to achieve the best possible performance. Finally, by examining a learning curve, an experiment is made to find out whether the recording of further training data would be reasonable.
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title_short	Hands on Wheel Classification Based on Depth Images and Neural Networks
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