Pose Estimation for Distracted Driver Detection Using Deep Convolutional Neural Networks

  • Siddhesh Thakur
  • Bhakti BahetiEmail author
  • Suhas Gajre
  • Sanjay Talbar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1019)


Distracted driver has been a major issue in today’s world with more than 1.25 million road incidents of fatality. Almost 20% of all the vehicle crashes occur due to distracted driver. We attempt to create a warning system which will make the driver attentive again. This paper focuses on a simple yet effective Convolutional Neural Network technique which can help us to detect if the driver is safely driving or is distracted which is a binary classification task. It would help in improving the safety measures of the driver and vehicle. We propose two techniques for distracted driver detection achieving state of the art results. We achieve an accuracy of 96.16% for the 10 class classification. We propose to deconstruct the problem into a binary classification problem and achieve an accuracy of 99.12% for the same. We take advantage of recent techniques of transfer learning combined with regularization techniques to achieve these results.


Distracted driver detection Driver pose estimation ADAS Deep learning 


  1. 1.
    Abouelnaga, Y., Eraqi, H.M., Moustafa, M.N.: Real-time distracted driver posture classification. CoRR abs/1706.09498 (2017).
  2. 2.
    Baheti, B., Gajre, S., Talbar, S.: Detection of distracted driver using convolutional neural network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2018Google Scholar
  3. 3.
    Das, N., Ohn-Bar, E., Trivedi, M.M.: On performance evaluation of driver hand detection algorithms: challenges, dataset, and metrics. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 2953–2958, September 2015.
  4. 4.
    Farm, S.: State farm distracted driver detection (2016).
  5. 5.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015).
  6. 6.
    Hssayeni, M., Saxena, S., Ptucha, R., Savakis, A.: Distracted driver detection: deep learning vs handcrafted features 2017, 20–26 (2017)Google Scholar
  7. 7.
    Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. CoRR abs/1502.03167 (2015).
  8. 8.
    Le, T.H.N., Zheng, Y., Zhu, C., Luu, K., Savvides, M.: 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), pp. 46–53, June 2016.
  9. 9.
    Maas, A.L.: Rectifier nonlinearities improve neural network acoustic models (2013)Google Scholar
  10. 10.
    Ng, A.Y.: Feature selection, l1 vs. l2 regularization, and rotational invariance. In: Proceedings of the Twenty-first International Conference on Machine Learning, ICML 2004, p. 78. ACM, New York (2004).
  11. 11.
    NHTSA: National highway traffic safety administration traffic safety facts.
  12. 12.
    Ohn-Bar, E., Martin, S., Tawari, A., Trivedi, M.M.: Head, eye, and hand patterns for driver activity recognition. In: 2014 22nd International Conference on Pattern Recognition, pp. 660–665, August 2014.
  13. 13.
    Seshadri, K., Juefei-Xu, F., Pal, D.K., Savvides, M., Thor, C.P.: 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), pp. 35–43, June 2015.
  14. 14.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014).
  15. 15.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014).
  16. 16.
    Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-resnet and the impact of residual connections on learning. CoRR abs/1602.07261 (2016).
  17. 17.
    Szegedy, C., et al.: Going deeper with convolutions. CoRR abs/1409.4842 (2014).
  18. 18.
    WHO: World health organization global status report on road safety 2015 (2015). Accessed 03 Apr 2018
  19. 19.
    Yan, C., Coenen, F., Zhang, B.: Driving posture recognition by convolutional neural networks. IET Comput. Vis. 10(2), 103–114 (2016). Scholar
  20. 20.
    Zhang, X., Zheng, N., Wang, F., He, Y.: 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, pp. 248–251, July 2011.
  21. 21.
    Zhao, C.H., Zhang, B.L., He, J., Lian, J.: Recognition of driving postures by contourlet transform and random forests. IET Intell. Transp. Syst. 6(2), 161–168 (2012). Scholar
  22. 22.
    Zhao, C.H., Zhang, B.L., Zhang, X.Z., Zhao, S.Q., Li, H.X.: Recognition of driving postures by combined features and random subspace ensemble of multilayer perceptron classifiers. Neural Comput. Appl. 22(1), 175–184 (2013). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Siddhesh Thakur
    • 1
  • Bhakti Baheti
    • 1
    Email author
  • Suhas Gajre
    • 1
  • Sanjay Talbar
    • 1
  1. 1.Shri Guru Gobind Singhji Institute of Engineering and TechnologyNandedIndia

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