Video surveillance for monitoring driver's fatigue and distraction

Fatigue and distraction effects in drivers represent a great risk for road safety. For both types of driver behavior problems, image analysis of eyes, mouth and head movements gives valuable information. We present in this paper a system for monitoring fatigue and distraction in drivers by evaluating their performance using image processing. We extract visual features related to nod, yawn, eye closure and opening, and mouth movements to detect fatigue as well as to identify diversion of attention from the road. We achieve an average of 98.3% and 98.8% in terms of sensitivity and specificity for detection of driver's fatigue, and 97.3% and 99.2% for detection of driver's distraction when evaluating four video sequences with different drivers

Modern drowsiness detection techniques: a review

According to recent statistics, drowsiness, rather than alcohol, is now responsible for one-quarter of all automobile accidents. As a result, many monitoring systems have been created to reduce and prevent such accidents. However, despite the huge amount of state-of-the-art drowsiness detection systems, it is not clear which one is the most appropriate. The following points will be discussed in this paper: Initial consideration should be given to the many sorts of existing supervised detecting techniques that are now in use and grouped into four types of categories (behavioral, physiological, automobile and hybrid), Second, the supervised machine learning classifiers that are used for drowsiness detection will be described, followed by a discussion of the advantages and disadvantages of each technique that has been evaluated, and lastly the recommendation of a new strategy for detecting drowsiness

Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking

It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video and image processing techniques to detect the drivers’ eye condition. Four features of the eye were extracted to determine the condition of drowsiness, the percentage of eyelid closure (PERCLOS), blink frequency, maximum closure duration of the eyes, and eye aspect ratio (ARE). These parameters were then integrated into an ANN and combined with the proposed method (gray wolf optimizer with ANN [GWOANN]) for drowsiness classification. The accuracy of these models was calculated, and the results demonstrate that the proposed method is the best. An Adadelta optimizer with 3 and 4 hidden layer networks of (13, 9, 7, and 5) and (200, 150, 100, 50, and 25) neurons was utilized. The GWOANN technique had 91.18% and 97.06% accuracy, whereas the ANN model had 82.35% and 86.76%

The effect of electronic word of mouth communication on purchase intention moderate by trust: a case online consumer of Bahawalpur Pakistan

The aim of this study is concerned with improving the previous research finding complete filling the research gaps and introducing the e-WOM on purchase intention and brand trust as a moderator between the e-WOM, and purchase intention an online user in Bahawalpur city Pakistan, therefore this study was a focus at linking the research gap of previous literature of past study based on individual awareness from the real-life experience. we collected data from the online user of the Bahawalpur Pakistan. In this study convenience sampling has been used to collect data and instruments of this study adopted from the previous study. The quantitative research methodology used to collect data, survey method was used to assemble data for this study, 300 questionnaire were distributed in Bahawalpur City due to the ease, reliability, and simplicity, effective recovery rate of 67% as a result 202 valid response was obtained for the effect of e-WOM on purchase intention and moderator analysis has been performed. Hypotheses of this research are analyzed by using Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The result of this research is e-WOM significantly positive effect on purchase intention and moderator role of trust significantly affects the relationship between e-WOM, and purchase intention. The addition of brand trust in the model has contributed to the explanatory power, some studied was conduct on brand trust as a moderator and this study has contributed to the literature in this favor. significantly this study focused on current marketing research. Unlike past studies focused on western context, this study has extended the regional literature on e-WOM, and purchase intention to be intergrading in Bahawalpur Pakistan context. Lastly, future studies are recommended to examine the effect of trust in other countries allow for the comparison of the findings

Low-cost vehicle driver assistance system for fatigue and distraction detection

In recent years, the automotive industry is equipping vehicles with sophisticated, and often, expensive systems for driving assistance. However, this vehicular technology is more focused on facilitating the driving and not in monitoring the driver. This paper presents a low-cost vehicle driver assistance system for monitoring the drivers activity that intends to prevent an accident. The system consists of 4 sensors that monitor physical parameters and driver position. From these values, the system generates a series of acoustic signals to alert the vehicle driver and avoiding an accident. Finally the system is tested to verify its proper operation.This work has been partially supported by the “Programa para la Formación de Personal Investigador–(FPI-2015-S2-884)” by the “Universitat Politècnica de València”.Sendra, S.; García-García, L.; Jimenez, JM.; Lloret, J. (2017). Low-cost vehicle driver assistance system for fatigue and distraction detection. En Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Verlag. 69-78. doi:10.1007/978-3-319-51207-5_7S6978Mapfre Foundation. (Online Article) Seguridad activa y pasiva. www.seguridadvialenlaempresa.com/seguridad-empresas/actualidad/noticias/seguridad-vial-activa-y-pasiva-2.jsp . Accessed 25 Aug 2016Dirección general de tráfico, Ministerio del Interior, Spanish Government. (Online Article) Las principales cifras de la siniestralidad vial. España 2014, p. 21 (2014). http://www.dgt.es/es/seguridad-vial/estadisticas-e-indicadores/publicaciones/ . Accessed 25 Aug 2016Fukuhara, H.: Vehicle collision alert system. US Patent 5355118 A, 11 Oct 1994Dirección general de tráfico, Ministerio del Interior, Spanish Government. (Online Article) Anuario estadístico de accidentes 2014, p. 10 (2014). http://www.dgt.es/es/seguridad-vial/estadisticas-e-indicadores/publicaciones/anuario-estadistico-general/ . Accessed 25 Aug 2016Dirección general de tráfico, Ministerio del Interior, Spanish Government. (Online Article) Otros factores de riesgo: La fatiga. http://www.dgt.es/PEVI/documentos/catalogo_recursos/didacticos/did_adultas/fatiga.pdf . Accessed 25 Aug 2016Seeing machines web page. https://www.seeingmachines.com/ . Accessed 25 Aug 2016Sigari, M.H., Pourshahabi, M.R., Soryani, M., Fathy, M.: A review on driver face monitoring systems for fatigue and distraction detection. Int. J. Adv. Sci. Technol. 64, 73–100 (2014). http://dx.doi.org/10.14257/ijast.2014.64.07Kutila, M., Jokela, M., Markkula, G., Romera Rue, M.: Driver distraction detection with a camera vision system. In: 14th IEEE International Conference on Image Processing (ICIP 2007), San Antonio, TX, USA, 16–19 September 2007. doi: 10.1109/ICIP.2007.4379556Rezaei, M., Klette, R.: 3D cascade of classifiers for open and closed eye detection in driver distraction monitoring. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011. LNCS, vol. 6855, pp. 171–179. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23678-5_19Mbouna, R.O., Kong, S.G., Chun, M.G.: Visual analysis of eye state and head pose for driver alertness monitoring. IEEE Trans. Intell. Transp. Syst. 14(3), 1462–1469 (2013). doi: 10.1109/TITS.2013.2262098Wahlstrom, E., Masoud, O., Papanikolopoulos, N.: Vision-based methods for driver monitoring. In: Proceedings of the International Conference on Intelligent Transportation Systems, vol. 2, pp. 903–908 (2003)Cherrat, L., Ezziyyani, M., El Mouden, A., Hassar, M.: Security and surveillance system for drivers based on user profile and learning systems for face recognition. Netw. Protoc. Algorithms 7(1), 98–118 (2015). doi: http://dx.doi.org/10.5296/npa.v7i1.7151Dong, Y., Hu, Z., Uchimura, K., Murayama, N.: Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans. Intell. Transp. Syst. 12(2), 596–614 (2011). doi: 10.1109/TITS.2010.2092770Force Sensitive Resistor features. http://www.trossenrobotics.com/productdocs/2010-10-26-DataSheet-FSR402-Layout2.pdf . Accessed 25 Aug 2016Louiza, M., Samira, M.: A new framework for request-driven data harvesting in vehicular sensor networks. Netw. Protoc. Algorithms 5(4), 1–18 (2013)Yao, H., Si, P., Yang, R., Zhang, Y.: Dynamic spectrum management with movement prediction in vehicular ad hoc networks. Ad Hoc Sens. Wirel. Netw. 32(1), 79–97 (2016

Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes