Driver drowsiness detection system using hybrid approach of convolutional neural network and bidirectional long short term memory (CNN_BILSTM)
In today’s world driver drowsiness is a major reason for fatal accidents of on road vehicles. Developing an automated, real-time drowsiness detection system is essential to provide accurate and timely alerts to the driver. In the proposed system, hybrid approach of CNN (Convolutional Neural Network)...
Gespeichert in:
| Veröffentlicht in: | Materials today : proceedings Jg. 45; S. 2897 - 2901 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
2021
|
| Schlagworte: | |
| ISSN: | 2214-7853, 2214-7853 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | In today’s world driver drowsiness is a major reason for fatal accidents of on road vehicles. Developing an automated, real-time drowsiness detection system is essential to provide accurate and timely alerts to the driver. In the proposed system, hybrid approach of CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long Term Dependencies) is used to detect the driver’s drowsiness. Video camera is used to track the facial image and eye blinks of the driver. The proposed system works in three main phases: In the First phase, driver's face image is Identified and observed using a web camera. In the Second phase, the eye image features are extracted using the Euclidean algorithm. During the third phase, the eye blinks are continually monitored. The final stage decides whether the measure in eye square is closed state or open state. When a driver falls asleep, there will be a warning message to alert the driver to prevent road accidents. |
|---|---|
| AbstractList | In today’s world driver drowsiness is a major reason for fatal accidents of on road vehicles. Developing an automated, real-time drowsiness detection system is essential to provide accurate and timely alerts to the driver. In the proposed system, hybrid approach of CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long Term Dependencies) is used to detect the driver’s drowsiness. Video camera is used to track the facial image and eye blinks of the driver. The proposed system works in three main phases: In the First phase, driver's face image is Identified and observed using a web camera. In the Second phase, the eye image features are extracted using the Euclidean algorithm. During the third phase, the eye blinks are continually monitored. The final stage decides whether the measure in eye square is closed state or open state. When a driver falls asleep, there will be a warning message to alert the driver to prevent road accidents. |
| Author | Priya, S. Rajamohana, S.P. Sangeetha, S. Radhika, E.G. |
| Author_xml | – sequence: 1 givenname: S.P. surname: Rajamohana fullname: Rajamohana, S.P. email: spr.it@psgtech.ac.in – sequence: 2 givenname: E.G. surname: Radhika fullname: Radhika, E.G. email: egr.it@psgtech.ac.in – sequence: 3 givenname: S. surname: Priya fullname: Priya, S. – sequence: 4 givenname: S. surname: Sangeetha fullname: Sangeetha, S. email: spa.it@psgtech.ac.in |
| BookMark | eNqFkM9OAjEQhxuDiYg-gZce9QC2u7BdDh4U_5EgHsRz022nUtzdkmmB8BI-s4t4MB70NJOZfr90vmPSqn0NhJxx1uOMZ5eLXqXiEnsJS5oJ7-XD_IC0k4T3uyIfpK0f_RE5DWHBGOODjOU8a5OPW3RrQGrQb4KrIQRqIIKOztc0bEOEiq6axRudbwt0hqrlEr3Sc-ot1b5e-3K1e6tKWsMKv0rceHynqja0cMbhPqzZlL6JCXOPkUbAilZQedzS89F0Km_Gk5fZ08UJObSqDHD6XTvk9f5uNnrsTp4fxqPrSVenLI1dK3ghCjB9nQmecWsNM7w_zAeJ0pplIjN6YAFYoa0VBQOhlcmt6guRNHfbYdohw32uRh8CgpXaRbX7Z0TlSsmZ3LmVC_nlVu7cSs5l47Zh01_sEl2lcPsPdbWnoDlr7QBl0A5qDXtD0nj3J_8JLombcQ |
| CitedBy_id | crossref_primary_10_1007_s11042_024_19738_z crossref_primary_10_1007_s13177_025_00537_1 crossref_primary_10_1016_j_measurement_2022_112266 crossref_primary_10_3390_s24175683 crossref_primary_10_3390_info16040294 crossref_primary_10_7717_peerj_cs_2594 crossref_primary_10_1007_s41870_023_01722_9 crossref_primary_10_1109_ACCESS_2024_3424654 crossref_primary_10_1109_ACCESS_2023_3288008 |
| Cites_doi | 10.1007/s12008-016-0349-9 10.1016/j.eswa.2013.07.108 10.1109/ICIP.2014.7026203 10.1007/s10015-014-0191-8 10.1016/j.biopsycho.2011.03.003 10.1016/j.medengphy.2013.07.011 10.1016/j.procs.2014.07.045 10.1109/INNOVATIONS.2016.7880030 10.1007/978-3-319-73450-7_53 10.1049/iet-its.2012.0032 10.1109/TITS.2013.2275192 10.1109/TBME.2018.2879346 10.3390/s18040957 10.1016/j.aap.2017.11.038 |
| ContentType | Journal Article |
| Copyright | 2020 |
| Copyright_xml | – notice: 2020 |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.matpr.2020.11.898 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2214-7853 |
| EndPage | 2901 |
| ExternalDocumentID | 10_1016_j_matpr_2020_11_898 S2214785320395742 |
| GroupedDBID | --M .~1 0R~ 1~. 4.4 457 4G. 5VS 7-5 8P~ AABXZ AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO ABMAC ABXDB ABYKQ ACDAQ ACGFS ACRLP ADBBV ADEZE AEBSH AEZYN AFKWA AFRZQ AFTJW AGHFR AGUBO AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BKOJK BLXMC EBS EFJIC EFLBG EJD FDB FIRID FYGXN GBLVA HZ~ KOM M41 NCXOZ O9- OAUVE P-8 P-9 PC. ROL SPC SPCBC SSM SSZ T5K ~G- AATTM AAXKI AAYWO AAYXX ABJNI ACLOT ACVFH ADCNI ADVLN AEIPS AEUPX AFJKZ AFPUW AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS |
| ID | FETCH-LOGICAL-c303t-f71b7bed4c67161ffd0d149852acc0676dc5fee0bcff7b0e7cad8fa4772608f93 |
| ISICitedReferencesCount | 17 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000659386000004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2214-7853 |
| IngestDate | Tue Nov 18 22:20:41 EST 2025 Sat Nov 29 06:53:05 EST 2025 Fri Feb 23 02:43:48 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Euclidean algorithm Eye dataset CNN and BiLSTM |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c303t-f71b7bed4c67161ffd0d149852acc0676dc5fee0bcff7b0e7cad8fa4772608f93 |
| PageCount | 5 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_matpr_2020_11_898 crossref_primary_10_1016_j_matpr_2020_11_898 elsevier_sciencedirect_doi_10_1016_j_matpr_2020_11_898 |
| PublicationCentury | 2000 |
| PublicationDate | 2021 2021-00-00 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – year: 2021 text: 2021 |
| PublicationDecade | 2020 |
| PublicationTitle | Materials today : proceedings |
| PublicationYear | 2021 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Gurudath, Riley (b0010) 2014; 34 B.N. Manu (2016, November). Facial features monitoring for real time drowsiness detection. In 2016 12th International Conference on Innovations in Information Technology (IIT) (pp. 1-4). IEEE. Zhang, Wang, Fu (b0005) 2014; 15 Fujiwara, Abe, Kamata, Nakayama, Suzuki, Yamakawa, Hiraoka, Kano, Sumi, Masuda, Matsuo, Kadotani (b0020) 2019; 66 Zhang (10.1016/j.matpr.2020.11.898_b0005) 2014; 15 Garcés Correa (10.1016/j.matpr.2020.11.898_b0045) 2014; 36 Jacobé de Naurois (10.1016/j.matpr.2020.11.898_b0025) 2019; 126 Izquierdo-Reyes (10.1016/j.matpr.2020.11.898_b0035) 2018; 12 Bharambe (10.1016/j.matpr.2020.11.898_b0065) 2015; 4 Gurudath (10.1016/j.matpr.2020.11.898_b0010) 2014; 34 10.1016/j.matpr.2020.11.898_b0030 10.1016/j.matpr.2020.11.898_b0040 10.1016/j.matpr.2020.11.898_b0070 10.1016/j.matpr.2020.11.898_b0015 10.1016/j.matpr.2020.11.898_b0055 Fujiwara (10.1016/j.matpr.2020.11.898_b0020) 2019; 66 Bando (10.1016/j.matpr.2020.11.898_b0060) 2015; 20 Jo (10.1016/j.matpr.2020.11.898_b0050) 2014; 41 Johnson (10.1016/j.matpr.2020.11.898_b0075) 2011; 87 |
| References_xml | – volume: 34 start-page: 400 year: 2014 end-page: 409 ident: b0010 article-title: Drowsy driving detection by EEG analysis using wavelet transform and K-means clustering publication-title: Procedia Computer Science – reference: B.N. Manu (2016, November). Facial features monitoring for real time drowsiness detection. In 2016 12th International Conference on Innovations in Information Technology (IIT) (pp. 1-4). IEEE. – volume: 15 start-page: 168 year: 2014 end-page: 177 ident: b0005 article-title: Automated detection of driver fatigue based on entropy and complexity measures publication-title: IEEE Trans. Intell. Transport. Syst. – volume: 66 start-page: 1769 year: 2019 end-page: 1778 ident: b0020 article-title: Heart rate variability-based driver drowsiness detection and its validation With EEG publication-title: IEEE Trans. Biomed. Eng. – volume: 12 start-page: 187 issue: 1 year: 2018 ident: 10.1016/j.matpr.2020.11.898_b0035 article-title: Advanced driver monitoring for assistance system (ADMAS): Based on emotions publication-title: Int. J. Interact. Des. Manuf. doi: 10.1007/s12008-016-0349-9 – volume: 41 start-page: 1139 issue: 4 year: 2014 ident: 10.1016/j.matpr.2020.11.898_b0050 article-title: Detecting driver drowsiness using feature-level fusion and user-specific classification publication-title: Exp. Syst. Appl. doi: 10.1016/j.eswa.2013.07.108 – ident: 10.1016/j.matpr.2020.11.898_b0055 doi: 10.1109/ICIP.2014.7026203 – volume: 20 start-page: 28 issue: 1 year: 2015 ident: 10.1016/j.matpr.2020.11.898_b0060 article-title: Detection of driver inattention from fluctuations in vehicle operating data publication-title: Artif. Life Robotics doi: 10.1007/s10015-014-0191-8 – volume: 4 start-page: 2202 issue: 1 year: 2015 ident: 10.1016/j.matpr.2020.11.898_b0065 article-title: Implementation of real time driver drowsiness detection system publication-title: Int. J. Sci. Res. (IJSR) – volume: 87 start-page: 241 issue: 2 year: 2011 ident: 10.1016/j.matpr.2020.11.898_b0075 article-title: Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model publication-title: Biol. Psychol. doi: 10.1016/j.biopsycho.2011.03.003 – volume: 36 start-page: 244 issue: 2 year: 2014 ident: 10.1016/j.matpr.2020.11.898_b0045 article-title: Automatic detection of drowsiness in EEG records based on multimodal analysis publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2013.07.011 – volume: 34 start-page: 400 year: 2014 ident: 10.1016/j.matpr.2020.11.898_b0010 article-title: Drowsy driving detection by EEG analysis using wavelet transform and K-means clustering publication-title: Procedia Computer Science doi: 10.1016/j.procs.2014.07.045 – ident: 10.1016/j.matpr.2020.11.898_b0015 doi: 10.1109/INNOVATIONS.2016.7880030 – ident: 10.1016/j.matpr.2020.11.898_b0040 doi: 10.1007/978-3-319-73450-7_53 – ident: 10.1016/j.matpr.2020.11.898_b0070 doi: 10.1049/iet-its.2012.0032 – volume: 15 start-page: 168 issue: 1 year: 2014 ident: 10.1016/j.matpr.2020.11.898_b0005 article-title: Automated detection of driver fatigue based on entropy and complexity measures publication-title: IEEE Trans. Intell. Transport. Syst. doi: 10.1109/TITS.2013.2275192 – volume: 66 start-page: 1769 issue: 6 year: 2019 ident: 10.1016/j.matpr.2020.11.898_b0020 article-title: Heart rate variability-based driver drowsiness detection and its validation With EEG publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2018.2879346 – ident: 10.1016/j.matpr.2020.11.898_b0030 doi: 10.3390/s18040957 – volume: 126 start-page: 95 year: 2019 ident: 10.1016/j.matpr.2020.11.898_b0025 article-title: Detection and prediction of driver drowsiness using artificial neural network models publication-title: Accid. Anal. Prevent. doi: 10.1016/j.aap.2017.11.038 |
| SSID | ssj0001560816 |
| Score | 2.3447194 |
| Snippet | In today’s world driver drowsiness is a major reason for fatal accidents of on road vehicles. Developing an automated, real-time drowsiness detection system is... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 2897 |
| SubjectTerms | CNN and BiLSTM Euclidean algorithm Eye dataset |
| Title | Driver drowsiness detection system using hybrid approach of convolutional neural network and bidirectional long short term memory (CNN_BILSTM) |
| URI | https://dx.doi.org/10.1016/j.matpr.2020.11.898 |
| Volume | 45 |
| WOSCitedRecordID | wos000659386000004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 2214-7853 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001560816 issn: 2214-7853 databaseCode: AIEXJ dateStart: 20180101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1fb9MwELeqjQdeEAgQ45_8wAOoJEpSN3YexygwBFWlFqlvUeLYarsurbqs2r4En4oPxp3tJB1UEyDxklbXJI58v9p3l9_dEfJKxIXQURZ6eY8xj3Hd9_Jcxp5MdE_EYcaCnJlmE3w4FNNpMup0ftS5MNslL0txdZWs_6uqQQbKxtTZv1B3c1MQwHdQOhxB7XD8I8W_3yDVoluAf-047YWqlO0Ibus2dy9NgGB2jdlaTVVxSzAvt-7hQHVY69J8GKa4ec2Qz-0eaE9YYqOiixlY8F1c4bvnSNs19IGT4TB9d_plPPlahxrqrlFZZScBjF5MQcGIRLuJtsT7bJGdr2aZzVgb-yO__aWYzc-MeOB_bMSjzfzandsEjTBvQlWzXbGLb0RtZKNJuWn5TbAqRlHIPC5shWFf7ZG5ZZ31d9dlYVnAbo_Hl8d79w8bylj44C2ssVpshHuKL2yj7F8Kc49xWBw1CvBlJwND4DDi4KAdkMPj08H0cxvrA7NSmC68zZPWBbAM1fC30fYbSTuGz-Q-uec8FnpskfaAdFT5kHy3KKMtymiDMmpRRg3KqEUZrVFGV5reQBm1KKMOZRRQRm-gjCLKqEEZRZRRizL6usXYm0fk24fB5OST51p7eBJspsrTPMx5rgomY3DYQ62LoABfXfSjTEowoOICWZAqyKXWPA8UlxmsKRkDXxCmUie9x-SgXJXqCaGhDqVQKk4KxlkgFTjwIAqSHCwtHkT6iET1XKbS1b3H9ivLtCY4LlKjgBQVAB5xCgo4Im-bi9a27Mvtp8e1klJnudp5SgFYt1349F8vfEbu4p_FBgOfk4Nqc6lekDtyW80vNi8dAH8ClxPEoQ |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Driver+drowsiness+detection+system+using+hybrid+approach+of+convolutional+neural+network+and+bidirectional+long+short+term+memory+%28CNN_BILSTM%29&rft.jtitle=Materials+today+%3A+proceedings&rft.au=Rajamohana%2C+S.P.&rft.au=Radhika%2C+E.G.&rft.au=Priya%2C+S.&rft.au=Sangeetha%2C+S.&rft.date=2021&rft.pub=Elsevier+Ltd&rft.issn=2214-7853&rft.eissn=2214-7853&rft.volume=45&rft.spage=2897&rft.epage=2901&rft_id=info:doi/10.1016%2Fj.matpr.2020.11.898&rft.externalDocID=S2214785320395742 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2214-7853&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2214-7853&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2214-7853&client=summon |