Abstract
Many road accidents are happening due to the negligent behaviour of the drivers, which increases the death rate day by day. The tiredness and drowsiness of the drivers are the primary cause of road accidents. Due to technological advancement, various techniques evolved to identify the drowsy state and alert the driver. As per the literature, the drowsiness detection techniques are categorized into three classes based on driving pattern, physiological characteristics and Computer vision. Among these techniques, we have focussed mainly on the Computer Vision technique in our survey due to its low cost and non-intrusive nature. This technique analyses the various images of driver’s posture, such as facial expression, yawning duration, head movement and eye closure to identify drowsy state. A detailed comparative study is presented in this paper and observed that spatial feature based techniques have given highest result with precision 97.12%. Also, state-of-the-art drowsiness detection techniques are exposed, analyzed and reviewed rigorously.
Similar content being viewed by others
References
Aaronson LS, Teel CS, Cassmeyer V, Neuberger GB, Pallikkathayil L, Pierce J, Press AN, Williams PD, Wingate A (1999) Defining and measuring fatigue. Image J Nurs Sch 31:45–50. https://doi.org/10.1111/j.1547-5069.1999.tb00420.x
Abtahi S, Hariri B, Shirmohammadi S (2011, May) Driver drowsiness monitoring based on yawning detection. In: 2011 IEEE International Instrumentation and Measurement Technology Conference, https://doi.org/10.1109/IMTC.2011.5944101
Albu, A. B., Widsten, B., Wang, T., Lan, J., & Mah, J. (2008, June). A computer vision-based system for real-time detection of sleep onset in fatigued drivers. In: 2008 IEEE intelligent vehicles symposium. IEEE. pp. 25-30. https://doi.org/10.1109/IVS.2008.4621133
Alioua N, Amine A, Rziza M (2014) Driver’s fatigue detection based on yawning extraction. Int Jo Veh Technol 2014:1–7
Alioua N, Amine A, Rogozan A, Bensrhair A, Rziza M (2016) Driver head pose estimation using efficient descriptor fusion. EURASIP J Image Video Process 2016(1):2. https://doi.org/10.1186/s13640-016-0103-z
Arefnezhad S, Samiee S, Eichberger A, Nahvi A (2019) Driver drowsiness detection based on steering wheel data applying adaptive neuro-fuzzy feature selection. Sensors 19(4):943. https://doi.org/10.3390/s19040943
Ayudhya CDN, Srinark T (2009, May) A method for real-time eye blink detection and its application. In: 6th international joint conference on computer science and software engineering (JCSSE). https://cpe.ku.ac.th/~jeab/papers/chinnawat_JCSSE2009.pdf
Bai X, Fang Y, Jia Y, Kan M, Shan S, Shen C, ..., Ji Q (Eds.) (2019). Video Analytics. Face and Facial Expression Recognition: Third International Workshop, FFER 2018, and Second International Workshop, DLPR 2018, Bei**g, China, August 20, 2018, Revised Selected Papers (Vol. 11264). Springer
Bamidele A, Kamardin K, Syazarin N, Mohd S, Shafi I, Azizan A, … Mad H (2019) Non-intrusive driver drowsiness detection based on face and eye tracking. Int J Adv Comput Sci Appl, https://pdfs.semanticscholar.org/06bb/08af9122e56679b29513b94ed754d9b028b2.pdf 10:549–569
Benoit A, Caplier A (2005, September) Hypovigilence analysis: open or closed eye or mouth? Blinking or yawning frequency?. In: IEEE Conference on Advanced Video and Signal Based Surveillance, https://doi.org/10.1109/AVSS.2005.1577268
Bergasa LM, Nuevo J, Sotelo MA, Barea R, Lopez ME (2006) Real-time system for monitoring driver vigilance. IEEE Trans Intell Transp Syst 7:63–77. https://doi.org/10.1109/TITS.2006.869598
Bhandari GM, Durge A, Bidwai A, Aware U (2014) Yawning analysis for driver drowsiness detection. Int J Res Eng Technol 3(2):502–505
Bouvier C, Benoit A, Caplier A, Coulon PY (2008, October) Open or closed mouth state detection: static supervised classification based on log-polar signature. In: International conference on advanced concepts for intelligent vision systems. Springer, Berlin, Heidelberg. pp. 1093-1102. https://doi.org/10.1007/978-3-540-88458-3_99
Bradski G, Kaehler A (2008) Learning OpenCV: Computer vision with the OpenCV library, O'Reilly Media, Inc
Chai M (2019) Drowsiness monitoring based on steering wheel status. Transp Res Part D: Transp Environ 66:95–103. https://doi.org/10.1016/j.trd.2018.07.007
Choudhary P, Sharma R, Singh G, Das S (2016) A survey paper on drowsiness detection & alarm system for drivers. Int Res J Eng Technol (IRJET) 3(12):1433–1437
Cui Y, Xu Y, Wu D (2019) EEG-based driver drowsiness estimation using feature weighted episodic training. IEEE Trans Neural Syst Rehabil Eng 27(11):2263–2273. https://doi.org/10.1109/TNSRE.2019.2945794
Cyganek B, Gruszczyński S (2014) Hybrid computer vision system for drivers' eye recognition and fatigue monitoring. Neurocomputing 126:78–94. https://doi.org/10.1016/j.neucom.2013.01.048
Danisman T, Bilasco IM., Djeraba C, Ihaddadene N (2010, October) Drowsy driver detection system using eye blink patterns. In: 2010 International conference on machine and web intelligence, IEEE, https://doi.org/10.1109/ICMWI.2010.5648121
Dasgupta A, George A, Happy SL, Routray A (2013) A vision-based system for monitoring the loss of attention in automotive drivers. IEEE Trans Intell Transp Syst 14:1825–1838. https://doi.org/10.1109/TITS.2013.2271052
Dasgupta A, Rahman D, Routray A (2018) A smartphone-based drowsiness detection and warning system for automotive drivers. IEEE Trans Intell Transp Syst 20(11):4045–4054. https://doi.org/10.1109/TITS.2018.2879609
Debener S, Emkes R, De Vos M, Bleichner M (2015) Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear. Sci Rep 5:16743. https://doi.org/10.1038/srep16743
Dinges DF, Grace R (1998) PERCLOS: a valid psychophysiological measure of alertness as assessed by psychomotor vigilance. US Dept. transportation, Federal Highway Admin., Washington. DC, tech. Rep. Publication no. FHWA-MCRT-98-006
Dinges DF, Mallis MM, Maislin G, Powell JW (1998) Evaluation of techniques for ocular measurement as an index of fatigue and as the basis for alertness management (no. DOT-HS-808-762). United States. National Highway Traffic Safety Administration. https://rosap.ntl.bts.gov/view/dot/2518. Accessed Dec 2020
Dong W, Cheng CQ, Kai L, Bao-Hua F (2011, September). The automatic control system of anti drunk-driving. In: 2011 International conference on electronics, Communications and Control (ICECC). https://doi.org/10.1109/ICECC.2011.6067708
Dornaika F, Khattar F, Reta J, Arganda-Carreras I, Hernandez M, Ruichek Y (2018) Image-based driver drowsiness detection. In: Video analytics. Face and facial expression recognition. Springer, Cham. pp. 61–71. https://doi.org/10.1007/978-3-030-12177-8_6
Eskandarian, A., & Mortazavi, A. (2007, June). Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection. In: 2007 IEEE intelligent vehicles symposium. IEEE. pp. 553-559. https://doi.org/10.1109/IVS.2007.4290173
Fletcher L, Petersson L, Zelinsky A (2003, June) Driver assistance systems based on vision in and out of vehicles. In: IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No. 03TH8683) . IEEE, https://doi.org/10.1109/IVS.2003.1212930
Forsman PM, Vila BJ, Short RA, Mott CG, Van Dongen HP (2013) Efficient driver drowsiness detection at moderate levels of drowsiness. Accid Anal Prev 50:341–350. https://doi.org/10.1016/j.aap.2012.05.005
Friedrichs F, Yang B (2010, June) Camera-based drowsiness reference for driver state classification under real driving conditions. In: 2010 IEEE intelligent vehicles symposium. IEEE. pp. 101-106. https://doi.org/10.1109/IVS.2010.5548039
Friedrichs F, Yang B (2010, August) Drowsiness monitoring by steering and lane data based features under real driving conditions. In: 2010 18th European signal processing conference. IEEE. pp. 209-213
García-García, M., Caplier, A., & Rombaut, M. (2018, June). Sleep deprivation detection for real-time driver monitoring using deep learning. In: International conference image analysis and recognition (pp. 435-442). Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_49
George A, Routray A (2016) Fast and accurate algorithm for eye localisation for gaze tracking in low-resolution images. IET Comput Vis 10(7):660–669. https://doi.org/10.1049/iet-cvi.2015.0316
Ghoddoosian R, Galib M, Athitsos V (2019) A realistic dataset and baseline temporal model for early drowsiness detection. In: Proceedings of the IEEE Conference on Computer Vision and PatternRecognitionWorkshops,https://openaccess.thecvf.com/contentCVPRW_2019/html/AMFG/Ghoddoosian_A_Realistic_Dataset_and_Baseline_Temporal_Model_for_Early_Drowsiness_CVPRW_2019_paper.html
Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning vol 1
Grace R, Byrne VE, Bierman DM, Legrand JM, Gricourt D, Davis BK, ..., Carnahan B (1998, October) A drowsy driver detection system for heavy vehicles. In: 17th DASC. AIAA/IEEE/SAE. Digital Avionics Systems Conference. Proceedings (Cat. No. 98CH36267) , IEEE, https://doi.org/10.1109/DASC.1998.739878
Guo JM, Markoni H (2019) Driver drowsiness detection using hybrid convolutional neural network and long short-term memory. Multimed Tools Appl 78(20):29059–29087. https://doi.org/10.1007/s11042-018-6378-6
Gurudath N, Riley HB (2014) Drowsy driving detection by EEG analysis using wavelet transform and K-means clustering. Procedia Comput Sci 34:400–409. https://doi.org/10.1016/j.procs.2014.07.045
Hammedi J, Ameur IB, Bazine S, Abdelalli AB (2020, July). Performance benchmarking of drowsiness detection methods. In: 2020 17th international multi-conference on systems, Signals & Devices (SSD). IEEE. pp. 179-184. https://doi.org/10.1109/SSD49366.2020.9364253
Han S, Yang S, Kim J, Gerla M (2012, February) EyeGuardian: a framework of eye tracking and blink detection for Mobile device users. In: Proceedings of the twelfth workshop on Mobile computing systems & applications. (pp. 1-6). https://doi.org/10.1145/2162081.2162090
Hansen DW, Ji Q (2009) In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans Pattern Anal Mach Intell 32:478–500. https://doi.org/10.1109/TPAMI.2009.30
He J, Roberson S, Fields B, Peng J, Cielocha S, Coltea J (2013) Fatigue detection using smartphones. J Ergon 3(03):1–7. https://doi.org/10.4172/2165-7556.1000120
Heo J, Savvides M (2011) Gender and ethnicity specific generic elastic models from a single 2D image for novel 2D pose face synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 34:2341–2350. https://doi.org/10.1109/TPAMI.2011.275
Hsu HT, Lee IH, Tsai HT, Chang HC, Shyu KK, Hsu CC, Chang HH, Yeh TK, Chang CY, Lee PL (2015) Evaluate the feasibility of using frontal SSVEP to implement an SSVEP-based BCI in young, elderly and ALS groups. IEEE Trans Neural Syst Rehabil Eng 24:603–615. https://doi.org/10.1109/TNSRE.2015.2496184
Hu T, Jha S, Busso C (2021) Temporal head pose estimation from point cloud in naturalistic driving conditions. IEEE Trans Intell Transp Syst:1–14. https://doi.org/10.1109/TITS.2021.3075350
Huang R, Wang Y, Guo L (2018, October) P-FDCN based eye state analysis for fatigue detection. In: 2018 IEEE 18th international conference on communication technology (ICCT). IEEE. (pp. 1174-1178) https://doi.org/10.1109/ICCT.2018.8599947
Ingre M, Åkerstedt T, Peters B, Anund A, Kecklund G (2006) Subjective sleepiness, simulated driving performance and blink duration: examining individual differences. J Sleep Res 15(1):47–53. https://doi.org/10.1111/j.1365-2869.2006.00504.x
Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37. https://doi.org/10.1109/34.824819
Jamshidi S, Azmi R, Sharghi M, Soryani M (2021) Hierarchical deep neural networks to detect driver drowsiness. Multimed Tools Appl 80(10):16045–16058. https://doi.org/10.1007/s11042-021-10542-7
Jayanthi, D., & Bommy, M. (2012). Vision-based real-time driver fatigue detection system for efficient vehicle control. International journal of engineering and advanced technology (IJEAT) ISSN, 2249-8958. http://doi.org/10.1.1.675.7655.
Jayaram V, Alamgir M, Altun Y, Scholkopf B, Grosse-Wentrup M (2016) Transfer learning in brain-computer interfaces. IEEE Comput Intell Mag 11(1):20–31. https://doi.org/10.1109/MCI.2015.2501545
Ji Q, Zhang L (2018, July) Mental fatigue detection based on multi-inter-domain optical flow characteristics. In: 2018 5th international conference on information science and control engineering (ICISCE), https://doi.org/10.1109/ICISCE.2018.00073
Jo J, Lee SJ, Kim J, Jung HG, Park KR (2011) Vision-based method for detecting driver drowsiness and distraction in driver monitoring system. Opt Eng 50(12):127202. https://doi.org/10.1117/1.3657506
Jo J, Lee SJ, Park KR, Kim IJ, Kim J (2014) Detecting driver drowsiness using feature-level fusion and user-specific classification. Expert Syst Appl 41(4):1139–1152. https://doi.org/10.1016/j.eswa.2013.07.108
Joshi A, Kyal S, Banerjee S, Mishra T (2020 Oct 21) In-the-wild drowsiness detection from facial expressions. In2020 IEEE intelligent vehicles symposium (IV). IEEE. pp. 207-212. https://doi.org/10.1109/IV47402.2020.9304579
Kaplan S, Guvensan MA, Yavuz AG, Karalurt Y (2015) Driver behavior analysis for safe driving: a survey. IEEE Trans Intell Transp Syst 16(6):3017–3032. https://doi.org/10.1109/TITS.2015.2462084
Kashiba Y, Tanaka Y, Tsuji T, Yamada N, Suetomi T (2009, November) Analysis of human hand impedance properties depending on driving conditions. In Proceedings: fifth international workshop on Computational Intelligence & Applications. IEEE SMC Hiroshima chapter. (Vol. 2009, no. 1, pp. 88-93). http://eprints.lib.okayama-u.ac.jp/19643. Accessed Dec 2020
Kidmose P, Looney D, Ungstrup M, Rank ML, Mandic DP (2013) A study of evoked potentials from ear-EEG. IEEE Trans Biomed Eng 60(10):2824–2830. https://doi.org/10.1109/TBME.2013.2264956
Koporec, G., Mandeljc, R., Kenk, V. S., Perš, J., Vuckovic, G., & Milic, R. (n.d.) Observation of Selected Human Physiological Parameters Using Computer Vision
Krithika LB, Priya GL (2021) Graph based feature extraction and hybrid classification approach for facial expression recognition. J Ambient Intell Humaniz Comput 12(2):2131–2147. https://doi.org/10.1007/s12652-020-02311-5
Lee SJ, Jo J, Jung HG, Park KR, Kim J (2011) Real-time gaze estimator based on driver's head orientation for forward collision warning system. IEEE Trans Intell Transp Syst 12(1):254–267. https://doi.org/10.1109/TITS.2010.2091503
Lee YH, Ahn H, Ahn HB, Lee SY (2019) Visual object detection and tracking using analytical learning approach of validity level. Intell Autom Soft Comput 25(1):205–215
Li Z, Li SE, Li R, Cheng B, Shi J (2017) Online detection of driver fatigue using steering wheel angles for real driving conditions. Sensors 17(3):495. https://doi.org/10.3390/s17030495
Li Y, Wang Y, Chen Z, Zhu Y, Li Y, Wang Y, … Zhu Y (2020) Visual relationship detection with contextual information. CMC-Comput Mater Contin 63(3):1575–1589. https://doi.org/10.32604/CMC.2020.07451, http://www.techscience.com/cmc/v63n3/38894
Lin CT, Chang CJ, Lin BS, Hung SH, Chao CF, Wang IJ (2010) A real-time wireless brain–computer interface system for drowsiness detection. IEEE Trans Biomed Circuits Syst 4:214–222. https://doi.org/10.1109/TBCAS.2010.2046415
Liu CC, Hosking SG, Lenné MG (2009) Predicting driver drowsiness using vehicle measures: recent insights and future challenges. J Saf Res 40(4):239–245. https://doi.org/10.1016/j.jsr.2009.04.005
Liu W, Sun H, Shen W (2010, April) Driver fatigue detection through pupil detection and yawing analysis. In: 2010 international conference on bioinformatics and biomedical technology. IEEE. pp. 404-407. https://doi.org/10.1109/ICBBT.2010.5478931
Liu A, Li Z, Wang L, Zhao Y (2010, September) A practical driver fatigue detection algorithm based on eye state. In: 2010 Asia Pacific conference on postgraduate research in microelectronics and electronics (PrimeAsia). IEEE. (pp. 235-238). https://doi.org/10.1109/PRIMEASIA.2010.5604919
Liu A, Li Z, Wang L, Zhao Y (2010, September). A practical driver fatigue detection algorithm based on eye state. In: 2010 Asia Pacific Conference on Postgraduate Research in Microelectronics andElectronics. https://doi.org/10.1109/PRIMEASIA.2010.5604919
Liu W, Qian J, Yao Z, Jiao X, Pan J (2019) Convolutional two-stream network using multi-facial feature fusion for driver fatigue detection. Future Internet 11. https://doi.org/10.3390/fi11050115
Liu Z, Peng Y, Hu W (2020) Driver fatigue detection based on deeply-learned facial expression representation. J Vis Commun Image Represent 71:102723. https://doi.org/10.1016/j.jvcir.2019.102723
Lv X, Su M, Wang Z (2021) Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems. Microprocess Microsyst:104034. https://doi.org/10.1016/j.micpro.2021.104034
Maior CBS, das Chagas Moura MJ, Santana JMM, Lins ID (2020) Real-time classification for autonomous drowsiness detection using eye aspect ratio. Expert Syst Appl 158:113505. https://doi.org/10.1016/j.eswa.2020.113505
Malla, A. M., Davidson, P. R., Bones, P. J., Green, R., & Jones, R. D. (2010, August). Automated video-based measurement of eye closure for detecting behavioral microsleep. In: 2010 annual international conference of the IEEE engineering in medicine and biology. IEEE. pp. 6741-6744. https://doi.org/10.1109/IEMBS.2010.5626013
Malla AM, Davidson PR, Bones PJ, Green R, Jones RD (2010, August) Automated video-based measurement of eye closure for detecting behavioral microsleep. In: 2010 annual international conference of the IEEE engineering in medicine and biology (pp. 6741-6744). IEEE. https://doi.org/10.1109/IEMBS.2010.5626013
Manoharan K, Daniel P (2018) Survey on various lane and driver detection techniques based on image processing for hilly terrain. IET Image Process 12(9):1511–1520. https://doi.org/10.1049/iet-ipr.2017.0864
Mavely AG, Judith JE, Sahal PA, Kuruvilla SA (2017, December) Eye gaze tracking based driver monitoring system. In: 2017 IEEE international conference on circuits and systems (ICCS), https://doi.org/10.1109/ICCS1.2017.8326022
Miah AA, Ahmad M, Mim KZ (2020) Drowsiness detection using eye-blink pattern and mean eye landmarks’ distance. In: Proceedings of international joint conference on computational intelligence. Springer, Singapore. pp. 111–121. https://doi.org/10.1007/978-981-13-7564-4_10
Mittal A, Kumar K, Dhamija S, Kaur M (2016, March) Head movement-based driver drowsiness detection: a review of state-of-art techniques. In: 2016 IEEE international conference on engineering and technology (ICETECH). IEEE. (pp. 903-908). https://doi.org/10.1109/ICETECH.2016.7569378
Nair V, Charniya N (2018, May) Drunk driving and drowsiness detection alert system. In: International conference on ISMAC in computational vision and bio-engineering. Springer, Cham. pp. 1191-1207. https://doi.org/10.1007/978-3-030-00665-5_113
Naqvi RA, Arsalan M, Batchuluun G, Yoon HS, Park KR (2018) Deep learning-based gaze detection system for automobile drivers using a NIR camera sensor. Sensors 18(2):456. https://doi.org/10.3390/s18020456
Ngxande M, Tapamo JR, Burke M (2017, November) Driver drowsiness detection using behavioral measures and machine learning techniques: a review of state-of-art techniques. In: 2017 pattern recognition Association of South Africa and Robotics and mechatronics (PRASA-RobMech). IEEE. pp. 156-161. https://doi.org/10.1109/RoboMech.2017.8261140
Niloy AR, Chowdhury AI, Sharmin N (2020) A brief review on different Driver's drowsiness detection techniques. Int J Image Graphics Signal Process 12(3):41. https://doi.org/10.5815/ijigsp.2020.03.05
Norton JJ, Lee DS, Lee JW, Lee W, Kwon O, Won P, … Rogers JA (2015) Soft, curved electrode systems capable of integration on the auricle as a persistent brain–computer interface. Proc Natl Acad Sci 112(13):3920–3925. https://doi.org/10.1073/pnas.1424875112
Nugraha BT, Sarno R, Asfani DA, Igasaki T, Munawar MN (2016) CLASSIFICATION OF DRIVER FATIGUE STATE BASED ON EEG USING EMOTIV EPOC+. J Theor Appl Inf Technol 86(3) http://www.jatit.org/volumes/Vol86No3/3Vol86No3.pdf
Omidyeganeh M, Shirmohammadi S, Abtahi S, Khurshid A, Farhan M, Scharcanski J, Hariri B, Laroche D, Martel L (2016) Yawning detection using embedded smart cameras. IEEE Trans Instrum Meas 65(3):570–582. https://doi.org/10.1109/TIM.2015.2507378
Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359. https://doi.org/10.1109/TKDE.2009.191
Pandey NN., Muppalaneni NB (2021, March) Real-time drowsiness identification based on eye state analysis. In: 2021 international conference on artificial intelligence and smart systems (ICAIS). IEEE. pp. 1182-1187. https://doi.org/10.1109/ICAIS50930.2021.9395975
Pandey NN, Muppalaneni NB (2021) Temporal and spatial feature based approaches in drowsiness detection using deep learning technique. J Real-Time Image Proc 18:2287–2299. https://doi.org/10.1007/s11554-021-01114-x
Panicker AD, Nair MS (2017) Open-eye detection using iris–sclera pattern analysis for driver drowsiness detection. Sādhanā 42(11):1835–1849. https://doi.org/10.1007/s12046-017-0728-3
Park EJ (2008) Sensor report—MQ-3 Gas sensor
Park S, Pan F, Kang S, Yoo CD (2016, November) Driver drowsiness detection system based on feature representation learning using various deep networks. In: Asian conference on computer vision. Springer, Cham. pp. 154-164. https://doi.org/10.1007/978-3-319-54526-4_12
Picot A, Charbonnier S, Caplier A (2010, May) Drowsiness detection based on visual signs: blinking analysis based on high frame rate video. In: 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings. IEEE. (pp. 801-804). https://doi.org/10.1109/IMTC.2010.5488257
Ramzan M, Khan HU, Awan SM, Ismail A, Ilyas M, Mahmood A (2019) A survey on state-of-the-art drowsiness detection techniques. IEEE Access 7:61904–61919. https://doi.org/10.1109/ACCESS.2019.2914373
Reddy B, Kim YH, Yun S, Seo C, Jang J (2017) Real-time driver drowsiness detection for embedded system using model compression of deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. https://openaccess.thecvf.com/content_cvpr_2017_workshops/w4/papers/Reddy_Real-Time_Driver_Drowsiness_CVPR_2017_paper.pdf
Ren Z, Li R, Chen B, Zhang H, Ma Y, Wang C, … Zhang Y (2021) EEG-based driving fatigue detection using a two-level learning hierarchy radial basis function. Front Neurorobot 15. https://doi.org/10.3389/fnbot.2021.618408
Rezaei M, Klette R (2014) Look at the driver, look at the road: no distraction! No accident!. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 129-136. https://openaccess.thecvf.com/content_cvpr_2014/papers/Rezaei_Look_at_the_2014_CVPR_paper.pdf
Rongben W, Lie G, Bingliang T, Lisheng J (2004, October) Monitoring mouth movement for driver fatigue or distraction with one camera. In: proceedings. The 7th international IEEE conference on intelligent transportation systems, https://doi.org/10.1109/ITSC.2004.1398917
Sabet M, Zoroofi RA, Sadeghniiat-Haghighi K, Sabbaghian M (2012, May). A new system for driver drowsiness and distraction detection. In 20th Iranian conference on electrical engineering (ICEE2012) IEEE. https://doi.org/10.1109/IranianCEE.2012.6292547
Saradadevi M, Bajaj P (2008) Driver fatigue detection using mouth and yawning analysis. Int J Comput Sci Netw Secur 8(6):183–188. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.542.1708&rep=rep1&type=pdf. Accessed Dec 2020
Shahverdy M, Fathy M, Berangi R, Sabokrou M (2020) Driver behavior detection and classification using deep convolutional neural networks. Expert Syst Appl 149:113240. https://doi.org/10.1016/j.eswa.2020.113240
Shakeel MF, Bajwa NA, Anwaar AM, Sohail A, Khan A (2019, June) Detecting driver drowsiness in real time through deep learning based object detection. In: International work-conference on artificial neural networks. Springer, Cham. pp. 283-296. https://doi.org/10.1007/978-3-030-20521-8_24
Shamsuddin MRB, Sahar NNBS, Rahmat MHB (2017, November) Eye detection for drowsy driver using artificial neural network. In: International Conference on Soft Computing in Data Science Springer, Singapore, https://doi.org/10.1007/978-981-10-7242-0_10
Shih TH, Hsu CT (2016, November) MSTN: multistage spatial-temporal network for driver drowsiness detection. In: Asian conference on computer vision. Springer, Cham. pp. 146-153. https://doi.org/10.1007/978-3-319-54526-4_11
Simon M, Schmidt EA, Kincses WE, Fritzsche M, Bruns A, Aufmuth C, Bogdan M, Rosenstiel W, Schrauf M (2011) EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions. Clin Neurophysiol 122:1168–1178. https://doi.org/10.1016/j.clinph.2010.10.044
Smith P, Shah M, da Vitoria Lobo N (2000, September) Monitoring head/eye motion for driver alertness with one camera. In: Proceedings 15th International Conference on Pattern Recognition ICPR-2000. https://doi.org/10.1109/ICPR.2000.902999
Smith P, Shah M, da Vitoria Lobo N (2003) Determining driver visual attention with one camera. IEEE Trans Intell Transp Syst 4(4):205–218. https://doi.org/10.1109/TITS.2003.821342
Soni R, Kumar B, Chand S (2019) Text detection and localization in natural scene images based on text awareness score. Appl Intell 49(4):1376–1405. https://doi.org/10.1007/s10489-018-1338-4
Sun X, Xu L, Yang J (2007, November) Driver fatigue alarm based on eye detection and gaze estimation. In: MIPPR 2007: automatic target recognition and image analysis; and multispectral image acquisition. International Society for Optics and Photonics. Vol. 6786, p. 678612. https://doi.org/10.1117/12.747671
Sun, C., Li, J. H., Song, Y., & **, L. (2014). Real-time driver fatigue detection based on eye state recognition. In: Applied mechanics and Materials (Vol. 457, pp. 944-952). Trans tech publications ltd. https://doi.org/10.4028/www.scientific.net/AMM.457-458.944
Tran D, Do HM, Sheng W, Bai H, Chowdhary G (2018) Real-time detection of distracted driving based on deep learning. IET Intell Transp Syst 12:1210–1219. https://doi.org/10.1049/iet-its.2018.5172
Tümen V, Yıldırım Ö, Ergen B (2018, April) Detection of driver drowsiness in driving environment using deep learning methods. In: 2018 electric electronics, computer science, biomedical Engineerings'Meeting(EBBT), https://doi.org/10.1109/EBBT.2018.839142
Venkata Phanikrishna B, Jaya Prakash A, Suchismitha C (2021) Deep review of machine learning techniques on detection of drowsiness using EEG signal. IETE J Res:1–16. https://doi.org/10.1080/03772063.2021.1913070
Vural E, Cetin M, Ercil A, Littlewort G, Bartlett M, Movellan J (2007, October) Drowsy driver detection through facial movement analysis. In International workshop on human-computer interaction. Springer, Berlin, Heidelberg. (pp. 6-18). https://doi.org/10.1007/978-3-540-75773-3_2
W. H. Organization et al. (2018) Road safety tech. Rep. World Health Organization. Regional Office for South-East Asia, https://www.who.int/publications/i/item/9789241565684
Wang JQ, Li SE, Zheng Y, Lu XY (2015) Longitudinal collision mitigation via coordinated braking of multiple vehicles using model predictive control. Integr Comput Aided Eng 22(2):171–185. https://doi.org/10.3233/ICA-150486
Wang YT, Nakanishi M, Wang Y, Wei CS, Cheng CK, Jung TP (2016) An online brain-computer interface based on SSVEPs measured from non-hair-bearing areas. IEEE Trans Neural Syst Rehabil Eng 25(1):14–21. https://doi.org/10.1109/TNSRE.2016.2573819
Wang Y, Huang R, Guo L (2019) Eye gaze pattern analysis for fatigue detection based on GP-BCNN with ESM. Pattern Recogn Lett 123:61–74. https://doi.org/10.1016/j.patrec.2019.03.013
Wang Y, ** L, Li K, Guo B, Zheng Y, Shi J (2019) Drowsy driving detection based on fused data and information granulation. IEEEAccess 7:183739–183750. https://doi.org/10.1109/ACCESS.2019.2960157
Wei CS, Wang YT, Lin CT, Jung TP (2018) Toward drowsiness detection using non-hair-bearing EEG-based brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 26:400–406. https://doi.org/10.1109/TNSRE.2018.2790359
Weng CH, Lai YH, Lai SH (2016, November) Driver drowsiness detection via a hierarchical temporal deep belief network. In: Asian conference on computer vision. Springer, Cham. pp. 117-133. https://doi.org/10.1007/978-3-319-54526-4_9
Wu JD, Chen TR (2008) Development of a drowsiness warning system based on the fuzzy logic images analysis. Expert Syst Appl 34:1556–1561. https://doi.org/10.1016/j.eswa.2007.01.019
Wu YC, **a YQ, **e P, Ji XW (2009, December) The design of an automotive anti-drunk driving system to guarantee the uniqueness of driver. In: 2009 international conference on information engineering and computer science. IEEE. pp. 1-4. https://doi.org/10.1109/ICIECS.2009.5364823
Wu D, Lawhern VJ, Gordon S, Lance BJ, Lin CT (2016) Driver drowsiness estimation from EEG signals using online weighted adaptation regularization for regression (OwARR). IEEE Trans Fuzzy Syst 25(6):1522–1535. https://doi.org/10.1109/TFUZZ.2016.2633379
Yan C, Wang Y, Zhang Z (2011) Robust real-time multi-user pupil detection and tracking under various illumination and large-scale head motion. Comput Vis Image Underst 115(8):1223–1238. https://doi.org/10.1016/j.cviu.2011.03.001
Yoshihara Y, Tanaka T, Osuga S, Fujikake K, Karatas N, Kanamori H (n.d.) Identifying high-risk older drivers by head-movement monitoring using a commercial driver monitoring camera. In 2020 IEEE intelligent vehicles symposium (IV) (pp. 1021-1028). IEEE. https://doi.org/10.1109/IV47402.2020.9304700
Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499–1503. https://doi.org/10.1109/LSP.2016.2603342
Funding
None.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pandey, N.N., Muppalaneni, N.B. A survey on visual and non-visual features in Driver’s drowsiness detection. Multimed Tools Appl 81, 38175–38215 (2022). https://doi.org/10.1007/s11042-022-13150-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-13150-1