Abstract
Road crashes have emerged as one of the top causes of death among the most productive age group. According to the World Health Organization (WHO), in the last decade 1.3 million people died and 5.3 million people got physically disabled in India due to road accidents. Many of these accidents are preventable because they happen due to driver distractions. In the past, many of the researchers have attempted to solve this problem by using traditional computer vision methods and deep learning to detect driver distractions, but an end-to-end system has not been presented. In this paper, we present a real-time system which performs driver distraction detection using convolutional neural network (CNN) and alerts the driver. The system is implemented using SDC (Software Defined Cockpit) powered by Android IVE (In-Vehicle-Experience). The system is flexible to run on Android smart phones for constrained environments. The detection is performed using the state of the art CNN models like MobileNetV1, MobileNetV2, InceptionV3 and VGG-16. We present the performance analysis of above mentioned CNN models using State Farm Distracted Driver Detection dataset. We extended system implementation and performance analysis of the best performing CNN model amongst the four tested, using our custom dataset to improve the performance for real-time. This custom data set is very close to the end driver scenarios as it covers not only the side view but also the frontal view of the driver, which improves the performance of the system significantly.
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References
World Health Organization, Violence and Injury Prevention, Global status report on road safety 2018. https://www.who.int/violence_injury_prevention/road_safety_status/2018/en. Accessed 14 Feb 2019
Center for disease control and prevention. https://www.cdc.gov/motorvehiclesafety/distracted_driving. Accessed 14 Feb 2019
State farm distracted driver detection. https://www.kaggle.com/c/state-farm-distracted-driver-detection Accessed 14 Feb 2019
National Highway Traffic Safety Administration, Distracted Driving. https://www.nhtsa.gov/risky-driving/distracted-driving. Accessed 14 Feb 2019
National Highway Traffic Safety Administration, Teen Driving. https://www.nhtsa.gov/road-safety/teen-driving. Accessed 14 Feb 2019
National Highway Traffic Safety Administration, U.S. Department of Transportation. https://www.nhtsa.gov/staticfiles/numbers/SafetyInNumbers_Nletter101_811742.pdf. Accessed 03 Sept 2019
SaveLIFE Foundation, Distracted Driving in India, A Study on Mobile Phone Usage, Pattern & Behaviour. http://savelifefoundation.org/wp-content/uploads/2017/04/Distracted-Driving-in-India_A-Study-on-Mobile-Phone-Usage-Pattern-and-Behaviour.pdf. Accessed 14 Feb 2019
Baheti, B., Gajre, S., Talbar, S.: Detection of distracted driver using convolutional neural network. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1145–11456. IEEE, Salt Lake City (2018)
Kim, W., Choi, H., Jang, B., Lim, J.: Driver distraction detection using single convolutional neural network. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1203–1205, IEEE, Jeju (2017)
Koesdwiady, A., Bedawi, S.M., Ou, C., Karray, F.: End-to-End deep learning for driver distraction recognition. In: Karray, F., Campilho, A., Cheriet, F. (eds.) Image Analysis and Recognition. ICIAR 2017, Lecture Notes in Computer Science, vol. 10317, pp. 11–18. Springer, Cham (2017)
Craye, C., Karray, F.: Driver distraction detection and recognition using RGB-D sensor. In: arXiv preprint. arXiv:1502.00250 (2015). https://arxiv.org/abs/1502.00250. Accessed 14 Feb 2019
Kutila, M., Jokela, M., Markkula, G., Rue, M.R.: Driver distraction detection with a camera vision system. In: 2007 IEEE International Conference on Image Processing, pp. VI-201–VI-204. IEEE, San Antonio (2007)
Howard, G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications. In: arXiv preprint, arXiv:1704.04861 (2017). https://arxiv.org/abs/1704.04861. Accessed 14 Feb 2019
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: arXiv preprint, arXiv:1409.1556 (2014). https://arxiv.org/abs/1409.1556. Accessed 14 Feb 2019
Ahlstrom, C., Kircher, K.: Review of real-time visual driver distraction detection algorithms. In: Barakova, E., Ruyter, B., Spink, A. (eds.) Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research, MB 2010, vol. 2, p. 4. ACM, New York (2010)
Abouelnaga, Y., Eraqi, H.M., Moustafa, M.N.: Real-time distracted driver posture classification. In: arXiv preprint, arXiv:1706.09498 (2018). https://arxiv.org/abs/1706.09498. Accessed 14 Feb 2019
Hssayeni, M.D., Saxena, S., Ptucha, R., Savakis, A.: Distracted driver detection: deep learning vs handcrafted features. In: Electronic Imaging, Imaging and Multimedia Analytics in a Web and Mobile World 2017, pp. 20–26(7). Society for Imaging Science and Technology (2017)
Yang, X., Lv, C., Zhang, Z., Wang, H., Na, X., Cao, D., Velenis, E., Wang, F.: Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition. IEEE Transact. Comput. Soc. Syst. 5(1), 95–108 (2018)
Sigari, M.H., Fathy, M., Soryani, M.: A driver face monitoring system for fatigue and distraction detection. Int. J. Veh. Technol. 2013, 1–11 (2013)
Hansen, J.H.L., Busso, C., Zheng, Y., Sathyanarayana, A.: Driver modeling for detection and assessment of driver distraction: examples from the UTDrive test bed. IEEE Signal Process. Mag. 34(4), 130–142 (2017)
Kutila, M.: Methods for machine vision based driver monitoring applications. In: PhD Thesis, VTT Publication Series, VTT Technical Research Centre of Finland, Tampere, Finland (2006)
Hansen, J.H.L., Boyraz, P., Takeda, K., Abut, H.: Digital Signal Processing for In-Vehicle Systems and Safety. Springer, New York (2012)
Moreno, R.J., Avilés Sánchez, O.F., Amaya, H.D.: Driver distraction detection using machine vision techniques. Ing. Compet. 16(2), 55–63 (2014)
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Kapoor, K., Pamula, R., Murthy, S.V. (2020). Real-Time Driver Distraction Detection System Using Convolutional Neural Networks. In: Singh, P., Panigrahi, B., Suryadevara, N., Sharma, S., Singh, A. (eds) Proceedings of ICETIT 2019. Lecture Notes in Electrical Engineering, vol 605. Springer, Cham. https://doi.org/10.1007/978-3-030-30577-2_24
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