In the last few years, the number of road accidents is increasing worldwide. According to the World Health Organization the most common cause behind these accidents is driver’s distraction and in many cases is caused by the use of a mobile phone. An attempt to develop a system for detecting distracted drivers and warn the responsible person against it was done. The system is a CNN based system that detects and identifies the cause of distraction. The base architecture for the CNN is VGG-16 and is modified for this task. Various activation functions (Leaky ReLU, DReLU, SELU) were used in order to investigate performance. Also, the performance of a lightweight attention module (squeeze-and-excitation) was evaluated. Experimental results show that the system outperforms earlier lightweight models in literature achieving an accuracy of 95.82%.
Real-Time Distracted Drivers Detection Using Deep Learning, American Journal of Artificial Intelligence.
Vol. 3, No. 1,
2019, pp. 1-8.
(2018). Global status report on road safety 2018. World Health Organization„ Geneva:. Licence : CC BYNC-SA 3.0 IGO.
Abouelnaga, Y., Eraqi, H., and Moustafa, M. (2017). Real-time distracted driver posture classification.
Baheti, B., Gajre, S., and Talbar, S. (2018). Detection of distracted driver using convolutional neural network. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 1145–11456.
Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L. (2008). Speeded-up robust features (surf). Comput. Vis. Image Underst., 110(3):346–359.
Center for Disease Control and Prevention (2018). Distracted Driving. [online] https://www.cdc.gov/motorvehiclesafety/distracted_driving/.
Clevert, D.-A., Unterthiner, T., and Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). Under Review of ICLR2016 (1997).
Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 886–893 vol. 1.
Das, N., Ohn-Bar, E., and Trivedi, M. M. (2015). On performance evaluation of driver hand detection algorithms: Challenges, dataset, and metrics. In 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pages 2953–2958.
Freund, Y. and Schapire, R. E. (1997). A decision theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci., 55(1):119–139.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778.
Hearst, M. A. (1998). Support vector machines. IEEE Intelligent Systems, 13(4):18–28.
Hssayeni, M., Saxena, S., Ptucha, R., and Savakis, A. (2017). Distracted driver detection: Deep learning vs handcrafted features. 2017:20–26.
Hu, J., Shen, L., and Sun, G. (2018). Squeeze-and-excitation networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7132– 7141.
Iglovikov, V. and Shvets, A. (2018). Ternausnet: Unet with vgg11 encoder pre-trained on imagenet for image segmentation.
Institutul National de Statistica (2017). Vehicule Inmatriculate si accidente de circulatie rutiera. [online] http://www.insse.ro/cms/sites/default/files/field/publicatii/vehicule_inmatriculate_in_circulatie_si_accidente_circulatie_rutiera_2017.pdf.
Ioffe, S. and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift.
Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S. (2017). Self-normalizing neural networks. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 971–980. Curran Associates, Inc.
Koesdwiady, A., Bedawi, S., Ou, C., and Karray, F. (2017). End-to-end deep learning for driver distraction recognition. pages 11–18.
Krizhevsky, A., Sutskever, I., and E. Hinton, G. (2012a). Imagenet classification with deep convolutional neural networks. Neural Information Processing Systems, 25.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012b). Imagenet classification with deep convolutional neural networks. In Pereira, F., Burges, C. J. C., Bottou, L., andWeinberger, K. Q., editors, Advances in Neural Information Processing Systems 25, pages 1097–1105. Curran Associates, Inc.
Le, T. H. N., Zheng, Y., Zhu, C., Luu, K., and Savvides, M. (2016). Multiple scale faster-rcnn approach to driver’s cell-phone usage and hands on steering wheel detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 46–53.
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541–551.
Li, H., Xiong, P., An, J., andWang, L. (2018). Pyramid attention network for semantic segmentation.
Maas, A. L., Hannun, A. Y., and Ng, A. Y. (2013). Rectifier nonlinearities improve neural network acoustic models. In in ICML Workshop on Deep Learning for Audio, Speech and Language Processing.
Macêdo, D., Zanchettin, C., Oliveira, A., and Ludermir, T. (2019). Enhancing batch normalized convolutional networks using displaced rectifier linear units: A systematic comparative study. Expert Systems with Applications, 124.
Maestas, D. R., Lumia, R., Starr, G., and Wood, J. (2010). Scale invariant feature transform (sift) parametric optimization using taguchi design of experiments. In Proceedings of the Third International Conference on Intelligent Robotics and Applications - Volume Part I, ICIRA’10, pages 630–641, Berlin, Heidelberg. Springer-Verlag.
Martin, S., Ohn-Bar, E., Tawari, A., and Trivedi, M. M. (2014). Understanding head and hand activities and coordination in naturalistic driving videos. In 2014 IEEE Intelligent Vehicles Symposium Proceedings, pages 884–889.
Masood, S., Rai, A., Aggarwal, A., Doja, M., and Ahmad, M. (2018). Detecting distraction of drivers using convolutional neural network. Pattern Recognition Letters.
National Highway Traffic Safety Administrator of United States (2018). Distracted Driving. [online] https: //www.nhtsa.gov/risky-driving/distracted-driving.
Ohn-Bar, E. (2013). Driver hand activity analysis in naturalistic driving studies: challenges, algorithms, and experimental studies. 22:1119.
Ohn-Bar, E., Martin, S., Tawari, A., and Trivedi, M. M. (2014). Head, eye, and hand patterns for driver activity recognition. In 2014 22nd International Conference on Pattern Recognition, pages 660–665.
Ohn-Bar, E. and Trivedi, M. (2013). In-vehicle hand activity recognition using integration of regions. In 2013 IEEE Intelligent Vehicles Symposium (IV), pages 1034–1039.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Proceedings of the 28th International Conference on Neural Information Processing Systems -Volume 1, NIPS’15, pages 91–99, Cambridge, MA, USA. MIT Press.
Ronneberger, O., Fischer, P., and Brox, T. (2015). Unet: Convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597.
Seshadri, K., Juefei-Xu, F., Pal, D. K., Savvides, M., and Thor, C. P. (2015). Driver cell phone usage detection on strategic highway research program (shrp2) face view videos. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 35– 43.
Sheng, W., Tran, D., Do, H., Bai, h., and Chowdhary, G. (2018). Real-time detection of distracted driving based on deep learning. IET Intelligent Transport Systems, 12.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition.
State Farm (2016). State Farm Distracted Driver Detection. [online] https://www.kaggle.com/c/state-farmdistracted-driver-detection.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. (2017). Attention is all you need. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 5998–6008. Curran Associates, Inc.
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y. (2015). Show, attend and tell: Neural image caption generation with visual attention. In Bach, F. and Blei, D., editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pages 2048–2057, Lille, France. PMLR.
Yan, C., Zhang, B., and Coenen, F. (2015). Driving posture recognition by convolutional neural networks. In 2015 11th International Conference on Natural Computation (ICNC), pages 680–685.
Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., and Li, S. (2017). S³fd: Single shot scale-invariant face detector.
Zhang, X., Zheng, N., Wang, F., and He, Y. (2011). Visual recognition of driver hand-held cell phone use based on hidden crf. In Proceedings of 2011 IEEE International Conference on Vehicular Electronics and Safety, pages 248–251.
Zhao, C. H., Zhang, B. L., He, J., and Lian, J. (2012). Recognition of driving postures by contourlet transform and random forests. IET Intelligent Transport Systems, 6(2):161–168.
Zhao, C. H., Zhang, B. L., Zhang, X. Z., Zhao, S. Q., and Li, H. X. (2013). Recognition of driving postures by combined features and random subspace ensemble of multilayer perceptron classifiers. Neural Computing and Applications, 22(1):175–184.