x

¿Aún no está registrado?

Cree su cuenta. Regístrese en Elsevier y obtendrá: información relevante, máxima actualización y promociones exclusivas.

Registrarme ahora
Solicitud de permisos - Ayuda - - Regístrese - Teléfono 902 888 740
Buscar en

FI 2016

0,500
© Thomson Reuters, Journal Citation Reports, 2016

Indexada en:

SCIE /JCR, Scopus, ScienceDirect

Métricas

  • Factor de Impacto: 0,500 (2016)
  • SCImago Journal Rank (SJR):0,212
  • Source Normalized Impact per Paper (SNIP):0,308

© Thomson Reuters, Journal Citation Reports, 2016

Revista Iberoamericana de Automática e Informática industrial 2017;14:307-28 - DOI: 10.1016/j.riai.2017.05.001
Sistema Automático Para la Detección de Distracción y Somnolencia en Conductores por Medio de Características Visuales Robustas
Automatic System to Detect Both Distraction and Drowsiness in Drivers Using Robust Visual Features
Alberto Fernández Villána,, , Rubén Usamentiaga Fernándezb, , Rubén Casado Tejedorb,
a Grupo TSK, Parque Científico y Tecnológico de Gijón, 33203 Gijón, Asturias, España
b Universidad de Oviedo, Campus de Viesques, 33204 Gijón, Asturias, España
Resumen

De acuerdo con un reciente estudio publicado por la Organización Mundial de la Salud (OMS), se estima que 1.25 millones de personas mueren como resultado de accidentes de tráfico. De todos ellos, muchos son provocados por lo que se conoce como inatención, cuyos principales factores contribuyentes son tanto la distracción como la somnolencia. En líneas generales, se calcula que la inatención ocasiona entre el 25% y el 75% de los accidentes y casi-accidentes. A causa de estas cifras y sus consecuencias se ha convertido en un campo ampliamente estudiado por la comunidad investigadora, donde diferentes estudios y soluciones han sido propuestos, pudiendo destacar los métodos basados en visión por computador como uno de los más prometedores para la detección robusta de estos eventos de inatención. El objetivo del presente artículo es el de proponer, construir y validar una arquitectura especialmente diseñada para operar en entornos vehiculares basada en el análisis de características visuales mediante el empleo de técnicas de visión por computador y aprendizaje automático para la detección tanto de la distracción como de la somnolencia en los conductores. El sistema se ha validado, en primer lugar, con bases de datos de referencia testeando los diferentes módulos que la componen. En concreto, se detecta la presencia o ausencia del conductor con una precisión del 100%, 90.56%, 88.96% por medio de un marcador ubicado en el reposacabezas del conductor, por medio del operador LBP, o por medio del operador CS-LBP, respectivamente. En lo que respecta a la validación mediante la base de datos CEW para la detección del estado de los ojos, se obtiene una precisión de 93.39% y de 91.84% utilizando una nueva aproximación basada en LBP (LBP_RO) y otra basada en el operador CS-LBP (CS-LBP_RO). Tras la realización de varios experimentos para ubicar la cámara en el lugar más adecuado, se posicionó la misma en el salpicadero, pudiendo aumentar la precisión en la detección de la región facial de un 86.88% a un 96.46%. Las pruebas en entornos reales se realizaron durante varios días recogiendo condiciones lumínicas muy diferentes durante las horas diurnas involucrando a 16 conductores, los cuales realizaron diversas actividades para reproducir síntomas de distracción y somnolencia. Dependiendo del tipo de actividad y su duración, se obtuvieron diferentes resultados. De manera general y considerando de forma conjunta todas las actividades se obtiene una tasa media de detección del 93.11%.

Abstract

According to the most recent studies published by the World Health Organization (WHO) in 2013, it is estimated that 1.25 million people die as a result of traffic crashes. Many of them are caused by what it is known as inattention, whose main contributing factors are both distraction and drowsiness. Overall, it is estimated that inattention causes between 25% and 75% of the crashes and near-crashes. That is why this is a thoroughly studied field by the research community, where solutions to combat distraction and drowsiness, in particular, and inattention, in general, can be classified into three main categories, and, where computer vision has clearly become a non-obtrusive effective tool for the detection of both distraction and drowsiness. The aim of this paper is to propose, build and validate an architecture based on the analysis of visual characteristics by using computer vision techniques and machine learning to detect both distraction and drowsiness in drivers. Firstly, the modules have been tested with all its components independently using several datasets. More specifically, the presence/absence of the driver is detected with an accuracy of 100%, 90.56%, 88.96% by using a marker positioned onto the headrest, the LBP operator and the CS-LBP operator, respectively. Regarding the eye closeness validation with CEW dataset, an accuracy of 93.39% and 91.84% is obtained using a new method using both LBP (LBP_RO) and CS-LBP (CS-LBP_RO). After performing several tests, the camera is positioned on the dashboard, increasing the accuracy of face detection from 86.88% to 96.46%. In connection with the tests performed in real-world settings, 16 drivers were involved performing several activities imitating different sings of sleepiness and distraction. Overall, an accuracy of 93.11%is obtained considering all activities and all drivers.

Palabras clave
Detección distracción y somnolencia, Visión por computador, Percepción y reconocimiento, Aprendizaje automático, Monitorización y supervisión
Keywords
Distraction and drowsiness detection, Computer vision, Perception and recognition, Machine learning, Monitoring and supervision
Referencias no citadas

Abtahi et al., 2014, Ahlstrom and Dukic, 2010, Ahonen et al., 2006, Asthana et al., 2011, Berri et al., 2014, Bolme et al., 2009, Boyraz et al., 2012, Chang and Lin, 2011, Dalal and Triggs, 2005, Daniluk et al., 2014, Dasgupta et al., 2013, Devi and Bajaj, 2008, Dinges and Grace, 1998, Dong et al., 2011, Fernandez et al., 2017, Fernández et al., 2015a, Fernández et al., 2015b, Fernández et al., 2015c, Fernández et al., 2016, Flores et al., 2010, Flores et al., 2011, Forsman et al., 2013, Hadid and Pietikäinen, 2013, Hammoud et al., 2005, Hansen and Ji, 2010, Hattori et al., 2006, Heikkilä et al., 2009, Hong and Qin, 2007, Hsu et al., 2003, Jain and Learned-Miller, 2010, Jo et al., 2014, Jung et al., 2016, Lee et al., 2011, Li et al., 2015, Liu et al., 2009, López Romero, 2016, Losada et al., 2013, Lu et al., 2011, Markuš et al., 2014, Martin, 2006, Mbouna et al., 2013, Murphy-Chutorian and Trivedi, 2010, Noori and Mikaeili, 2016, Nuevo et al., 2010, Ojala et al., 1996, Ojala et al., 2002, Organization, 2016, Pan et al., 2007, Peden et al., 2016, Phillips et al., 2000, RACE y l., 2016, Regan et al., 2011, Sahayadhas et al., 2012, Selvakumar et al., 2015, Shan, 2012, Shan et al., 2009, Sigari, 2009, Slawiñski et al., 2015, Song et al., 2013, Song et al., 2014, StopChatear, 2016, Talbot et al., 2013, Tan and Triggs, 2010, Timm and Barth, 2011, Uřičář et al., 2012, Vapnik, 1998, Vicente et al., 2015, Villan et al., 2016, Viola and Jones, 2004, Vural et al., 2007, You et al., 2013 and Zhang and Zhang, 2006.

Referencias
Abtahi et al., 2014
Abtahi, S., Omidyeganeh, M., Shirmohammadi, S., Hariri, B., 2014. Yawdd: a yawning detection dataset. In: Proceedings of the 5th ACM Multimedia Systems Conference. ACM, pp. 24-28.
Ahlstrom and Dukic, 2010
Ahlstrom, C., Dukic, T., 2010. Comparison of eye tracking systems with one and three cameras. In: Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research. ACM, p. 3.
Ahonen et al., 2006
T. Ahonen,A. Hadid,M. Pietikainen
Face description with local binary patterns: Application to face recognition
IEEE transactions on pattern analysis and machine intelligence, 28 (2006), pp. 2037-2041 http://dx.doi.org/10.1109/TPAMI.2006.244
Asthana et al., 2011
Asthana, A., Marks, T.K., Jones, M.J., Tieu, K.H., Rohith, M., 2011. Fully automatic pose-invariant face recognition via 3d pose normalization. In: 2011 International Conference on Computer Vision. IEEE, pp. 937-944.
Berri et al., 2014
Berri, R.A., Silva, A.G., Parpinelli, R.S., Girardi, E., Arthur, R., 2014. A pattern recognition system for detecting use of mobile phones while driving. In: Computer Vision Theory and Applications (VISAPP), 2014 International Conference on. Vol. 2. IEEE, pp. 411-418.
Bolme et al., 2009
Bolme, D.S., Draper, B.A., Beveridge, J.R., 2009. Average of synthetic exact filters. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, pp. 2105-2112.
Boyraz et al., 2012
Boyraz, P., Yang, X., Hansen, J.H., 2012. Computer vision systems for context-aware active vehicle safety and driver assistance. In: Digital Signal Processing for In-Vehicle Systems and Safety. Springer, pp. 217-227.
Chang and Lin, 2011
C.-C. Chang,C.-J. Lin
Libsvm: a library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST), 2 (2011), pp. 27
Dalal and Triggs, 2005
Dalal, N., Triggs, B., 2005. Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). Vol. 1. IEEE, pp. 886-893.
Daniluk et al., 2014
Daniluk, M., Rezaei, M., Nicolescu, R., Klette, R., 2014. Eye status based on eyelid detection: A driver assistance system. In: International Conference on Computer Vision and Graphics. Springer, pp. 171-178.
Dasgupta et al., 2013
A. Dasgupta,A. George,S. Happy,A. Routray,T. Shanker
An onboard vision based system for drowsiness detection in automotive drivers
International Journal of Advances in Engineering Sciences and Applied Mathematics, 5 (2013), pp. 94-103
Devi and Bajaj, 2008
Devi, M.S., Bajaj, P.R., 2008. Driver fatigue detection based on eye tracking. In: 2008 First International Conference on Emerging Trends in Engineering and Technology. IEEE, pp. 649-652.
Dinges and Grace, 1998
Dinges, D.F., Grace, R., 1998. Perclos: A valid psychophysiological measure of alertness as assessed by psychomotor vigilance. US Department of Transportation, Federal Highway Administration, Publication Number FHWA-MCRT-98-006.
Dong et al., 2011
Y. Dong,Z. Hu,K. Uchimura,N. Murayama
Driver inattention monitoring system for intelligent vehicles: A review
IEEE transactions on intelligent transportation systems, 12 (2011), pp. 596-614
Fernandez et al., 2017
A. Fernandez,J. Carus,R. Usamentiaga,E. Alvarez,R. Casado
Wearable and ambient sensors to health monitoring using computer vision and signal processing techniques
Journal of Networks, (2017),
In press
Fernández et al., 2015a
Fernández, A., Carús, J.L., Usamentiaga, R., Alvarez, E., Casado, R., 2015a. Unobtrusive health monitoring system using video-based physiological information and activity measurements. In: Computer, Information and Telecommunication Systems (CITS), 2015 International Conference on. IEEE, pp. 1-5.
Fernández et al., 2015b
Fernández, A., Casado, R., Usamentiaga, R., 2015b. A real-time big data architecture for glasses detection using computer vision techniques. In: Future Internet of Things and Cloud (FiCloud), 2015 3rd International Conference on. IEEE, pp. 591-596.
Fernández et al., 2015c
A. Fernández,R. García,R. Usamentiaga,R. Casado
Glasses detection on real images based on robust alignment
Machine Vision and Applications, 26 (2015), pp. 519-531
Fernández et al., 2016
A. Fernández,R. Usamentiaga,J.L. Carús,R. Casado
Driver distraction using visual-based sensors and algorithms
Sensors, 16 (2016), pp. 1805
Flores et al., 2010
M.J. Flores,J.M. Armingol,A. de la Escalera
Real-time warning system for driver drowsiness detection using visual information
Journal of Intelligent & Robotic Systems, 59 (2010), pp. 103-125 http://dx.doi.org/10.3390/genes8060168
Flores et al., 2011
M.J. Flores,A. de la Escalera
Sistema avanzado de asistencia a la conducción para la detección de la somnolencia
Revista Iberoamericana de Automática e Informática Industrial RIAI, 8 (2011), pp. 216-228
Forsman et al., 2013
P.M. Forsman,B.J. Vila,R.A. Short,C.G. Mott,H.P. Van Dongen
Efficient driver drowsiness detection at moderate levels of drowsiness
Accident Analysis & Prevention, 50 (2013), pp. 341-350
Hadid and Pietikäinen, 2013
A. Hadid,M. Pietikäinen
Demographic classification from face videos using manifold learning
Neurocomputing, 100 (2013), pp. 197-205
Hammoud et al., 2005
Hammoud, R.I., Wilhelm, A., Malawey, P., Witt, G.J., 2005. Eficient real-time algorithms for eye state and head pose tracking in advanced driver support systems. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. Vol. 2. IEEE, pp. 1181–vol.
Hansen and Ji, 2010
D.W. Hansen,Q. Ji
In the eye of the beholder: A survey of models for eyes and gaze
IEEE Transactions on pattern analysis and machine intelligence, 32 (2010), pp. 478-500 http://dx.doi.org/10.1109/TPAMI.2009.30
Hattori et al., 2006
Hattori, A., Tokoro, S., Miyashita, M., Tanaka, I., Ohue, K., Uozumi, S., 2006. Development of forward collision warning system using the driver behavioral information. Tech. rep., SAE Technical Paper.
Heikkilä et al., 2009
M. Heikkilä,M. Pietikäinen,C. Schmid
Description of interest regions with local binary patterns
Pattern recognition, 42 (2009), pp. 425-436
Hong and Qin, 2007
Hong, T., Qin, H., 2007. Drivers drowsiness detection in embedded system. In: Vehicular Electronics and Safety, 2007. ICVES. IEEE International Conference on. IEEE, pp. 1-5.
Hsu et al., 2003
Hsu, C.-W., Chang, C.-C., Lin, C.-J. et al., 2003. A practical guide to support vector classification.
Jain and Learned-Miller, 2010
Jain, V., Learned-Miller, E.G., 2010. Fddb: A benchmark for face detection in unconstrained settings. UMass Amherst Technical Report.
Jo et al., 2014
J. Jo,S.J. Lee,K.R. Park,I.-J. Kim,J. Kim
Detecting driver drowsiness using feature-level fusion and user-specific classification
Expert Systems with Applications, 41 (2014), pp. 1139-1152
Jung et al., 2016
J.-Y. Jung,S.-W. Kim,C.-H. Yoo,W.-J. Park,S.-J. Ko
Lbp-ferns-based feature extraction for robust facial recognition
IEEE Transactions on Consumer Electronics, 62 (2016), pp. 446-453
Lee et al., 2011
S.J. Lee,J. Jo,H.G. Jung,K.R. Park,J. Kim
Real-time gaze estimator based on driver's head orientation for forward collision warning system
IEEE Transactions on Intelligent Transportation Systems, 12 (2011), pp. 254-267
Li et al., 2015
Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G., 2015. A convolutional neural network cascade for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5325-5334.
Liu et al., 2009
C.C. Liu,S.G. Hosking,M.G. Lenné
Predicting driver drowsiness using vehicle measures: Recent insights and future challenges
Journal of safety research, 40 (2009), pp. 239-245 http://dx.doi.org/10.1016/j.jsr.2009.04.005
López Romero, 2016
López Romero, W.L., 2016. Sistema de control del estado de somnolencia en conductores de vehículos.
Losada et al., 2013
Losada, D.G., López, G.A. R., Acevedo, R.G., Villán, A.F., 2013. Aviueartificial vision to improve the user experience. In: New Concepts in Smart Cities: Fostering Public and Private Alliances (SmartMILE), 2013 International Conference on. IEEE, pp. 1-6.
Lu et al., 2011
Lu, L., Ning, X., Qian, M., Zhao, Y., 2011. Close eye detected based on synthesized gray projection. In: Advances in Multimedia, Software Engineering and Computing Vol. 2. Springer, pp. 345-351.
Markuš et al., 2014
Markuš, N., Frljak, M., Pandžić, I.S., Ahlberg, J., Forchheimer, R., 2014. Object detection with pixel intensity comparisons organized in decision trees. arXi**v preprint arXi***v:1305.4537.
Martin, 2006
E. Martin
Breakthrough research on real-world driver behavior released
National Highway Traffic Safety Administration, (2006)
Mbouna et al., 2013
R.O. Mbouna,S.G. Kong,M.-G. Chun
Visual analysis of eye state and head pose for driver alertness monitoring
IEEE transactions on intelligent transportation systems, 14 (2013), pp. 1462-1469
Murphy-Chutorian and Trivedi, 2010
E. Murphy-Chutorian,M.M. Trivedi
Head pose estimation and augmented reality tracking: An integrated system and evaluation for monitoring driver awareness
IEEE Transactions on intelligent transportation systems, 11 (2010), pp. 300-311
Noori and Mikaeili, 2016
S.M.R. Noori,M. Mikaeili
Driving drowsiness detection using fusion of electroencephalography, electrooculography, and driving quality signals
Journal of medical signals and sensors, 6 (2016), pp. 39
Nuevo et al., 2010
J. Nuevo,L.M. Bergasa,P. Jiménez
Rsmat: Robust simultaneous modeling and tracking
Pattern Recognition Letters, 31 (2010), pp. 2455-2463
of Transportation, D., 2016. Pennsylvania driver's manual. https://goo.gl/XCER8C, accessed: 2016-09-018
Ojala et al., 1996
T. Ojala,M. Pietikäinen,D. Harwood
A comparative study of texture measures with classification based on featured distributions
Pattern recognition, 29 (1996), pp. 51-59
Ojala et al., 2002
T. Ojala,M. Pietikainen,T. Maenpaa
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
IEEE Transactions on pattern analysis and machine intelligence, 24 (2002), pp. 971-987
Organization, 2016
Organization, W.H., 2016. Global status report on road safety 2015. http://goo.gl/jMoJ4l, accessed: 2016-07-01.
Pan et al., 2007
Pan, G., Sun, L., Wu, Z., Lao, S., 2007. Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on. IEEE, pp. 1-8.
Peden et al., 2016
M. Peden,T. Toroyan,E. Krug,K. Iaych
The status of global road safety: The agenda for sustainable development encourages urgent action
Journal of the Australasian College of Road Safety, 27 (2016), pp. 37
Phillips et al., 2000
P.J. Phillips,H. Moon,S.A. Rizvi,P.J. Rauss
The feret evaluation methodology for face-recognition algorithms
IEEE Transactions on pattern analysis and machine intelligence, 22 (2000), pp. 1090-1104
RACE y l., 2016
RACE, A. y. l. D., 2016. Los conductores españoles reconocen sufrir más somnolencia al volante que los usuarios europeos. http://goo.gl/mui9S3, accessed: 2016-07-01.
Regan et al., 2011
M.A. Regan,C. Hallett,C.P. Gordon
Driver distraction and driver inattention: Definition, relationship and taxonomy
Accident Analysis & Prevention, 43 (2011), pp. 1771-1781
Sahayadhas et al., 2012
A. Sahayadhas,K. Sundaraj,M. Murugappan
Detecting driver drowsiness based on sensors: a review
Sensors, 12 (2012), pp. 16937-16953 http://dx.doi.org/10.3390/s121216937
Selvakumar et al., 2015
K. Selvakumar,J. Jerome,K. Rajamani,N. Shankar
Real-time vision based driver drowsiness detection using partial least squares analysis
Journal of Signal Processing Systems, (2015), pp. 1-12
Shan, 2012
C. Shan
Learning local binary patterns for gender classification on real-world face images
Pattern Recognition Letters, 33 (2012), pp. 431-437
Shan et al., 2009
C. Shan,S. Gong,P.W. McOwan
Facial expression recognition based on local binary patterns: A comprehensive study
Image and Vision Compuing, 27 (2009), pp. 803-816
Sigari, 2009
Sigari, M.H., 2009. Driver hypo-vigilance detection based on eyelid behavior. In: Advances in Pattern Recognition, 2009. ICAPR’09. Seventh International Conference on. IEEE, pp. 426-429.
Slawiñski et al., 2015
E. Slawiñski,V. Mut,F. Penizzotto
Sistema de alerta al conductor basado en realimentación vibro-táctil
Revista Iberoamericana de Automática e Informática Industrial RIAI, 12 (2015), pp. 36-48
Song et al., 2013
F. Song,X. Tan,S. Chen,Z.-H. Zhou
A literature survey on robust and efficient eye localization in real-life scenarios
Pattern Recognition, 46 (2013), pp. 3157-3173
Song et al., 2014
F. Song,X. Tan,X. Liu,S. Chen
Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients
Pattern Recognition, 47 (2014), pp. 2825-2838
StopChatear, 2016
StopChatear, 2016. Uso de los smartphones en la conducción. http://goo.gl/67dvtn, accessed: 2016-07-01.
Talbot et al., 2013
R. Talbot,H. Fagerlind,A. Morris
Exploring inattention and distraction in the safetynet accident causation database
Accident Analysis & Prevention, 60 (2013), pp. 445-455
Tan and Triggs, 2010
X. Tan,B. Triggs
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE transactions on image processing, 19 (2010), pp. 1635-1650 http://dx.doi.org/10.1109/TIP.2010.2042645
Timm and Barth, 2011
F. Timm,E. Barth
Accurate eye centre localisation by means of gradients
VISAPP, 11 (2011), pp. 125-130
Uřičář et al., 2012
M. Uřičář,V. Franc,V. Hlaváč
Detector of facial landmarks learned by the structured output svm
VIsAPP, 12 (2012), pp. 547-556
Vapnik, 1998
Vapnik, V., 1998. Statistical learning theory wiley new york google scholar.
Vicente et al., 2015
F. Vicente,Z. Huang,X. Xiong,F. De la Torre,W. Zhang,D. Levi
Driver gaze tracking and eyes off the road detection system
IEEE Transactions on Intelligent Transportation Systems, 16 (2015), pp. 2014-2027
Villan et al., 2016
A.F. Villan,J.L.C. Candas,R.U. Fernandez,R.C. Tejedor
Face recognition and spoofing detection system adapted to visually-impaired people
IEEE Latin America Transactions, 14 (2016), pp. 913-921
Viola and Jones, 2004
P. Viola,M.J. Jones
Robust real-time face detection
International journal of computer vision, 57 (2004), pp. 137-154
Vural et al., 2007
Vural, E., Cetin, M., Ercil, A., Littlewort, G., Bartlett, M., Movellan, J., 2007. Drowsy driver detection through facial movement analysis. In: International Workshop on Human-Computer Interaction. Springer, pp. 6-18.
You et al., 2013
You, C.-W., Lane, N.D., Chen, F., Wang, R., Chen, Z., Bao, T.J., Montes-de Oca, M., Cheng, Y., Lin, M., Torresani, L. et al., 2013. Carsafe app: alerting drowsy and distracted drivers using dual cameras on smartphones. In: Proceeding of the 11th annual international conference on Mobile systems, applications, and services. ACM, pp. 13-26.
Zhang and Zhang, 2006
Zhang, Z., Zhang, J.-s., 2006. Driver fatigue detection based intelligent vehicle control. In: 18th International Conference on Pattern Recognition (ICPR’06). Vol. 2. IEEE, pp. 1262-1265.
Autor para correspondencia. (Alberto Fernández Villán alberto.fernandez@grupotsk.com)
Copyright © 2017