Abstract
The driver’s distracted attention will cause a huge safety hazard to the traffic. In different types of distraction, it is illegal to make phone calls and smoke while driving, which will be fined in China. In order to solve this problem, a method of driver’s distracted behavior detection based on channel attention convolution neural network is proposed. SE module is added to the Xception network, which can distinguish the importance of different feature channels and enhance the expression ability of the network. SE module mainly assigns different weights to features to enhance more important features and suppress less influential features. The experiment uses Xception and SE-Xception for comparison. The experimental results show that the accuracy of SE-Xception is 92.60%, which has a good performance for the distracted driving behavior detection of drivers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Choidhary, P., Velaga, N.R.: Mobile phone use during driving: effects on speed and effectiveness of driver compensatory behavior. Accid. Anal. Prev. 106, 370–378 (2017)
Kutila, M.H., Jokela, M., Mäkinen, T., Viitanen, J., Markkula, G., Victor, T.W.: Driver cognitive distraction detection: feature estimation and implementation. Proc. Inst. Mech. Eng. Part D: J. Automob. Eng. 221, 1027–1040 (2007)
Sahayadhas, A., Sundaraj, K., Murugappan, M., Palaniappanb, R.: A physiological measures-based method for detecting inattention in drivers using machine learning approach. Biocybern. Biomed. Eng. 35, 198–205 (2015)
Ebadi, Y., Fisher, D.L., Roberts, S.C.: Impact of cognitive distractions on drivers’ hazard anticipation behavior in complex scenarios. Transp. Res. Rec.: J. Transp. Res. Board 2673, 440–451 (2019)
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: The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas (2016)
Xing, Y., et al.: Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition. IEEE Trans. Comput. Soc. Syst. 5, 95–108 (2018)
Masood, S., Rai, A., Aggarwal, A., Doja, M.N., Ahmad, M.: Detecting distraction of drivers using convolutional neural network. Pattern Recognit. Lett. (2018)
Celaya-Padilla, J.M., et al.: Texting & driving detection using deep convolutional neural networks. Appl. Sci. 9, 2962 (2019)
Hu, J., Liu, W., Kang, J., Yang, W., Zhao, H.: Semi-cascade network for driver’s distraction recognition. Proc. Inst. Mech. Eng. Part D: J. Automob. Eng. 233, 2323–2332 (2019)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Szegedy, C., et al.: Going deeper with convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston (2015)
loffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. Proc. Mach. Learn. Res. 37, 448–456 (2015)
Szegedy, C., Vanhoucke, V., loffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas (2016)
Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: The Thirty-First AAAI Conference on Artificial Intelligence, San Francisco (2016)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu (2017)
Hu, J., Shen, L., Sun, G.: Squeeze-and-Excitation networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer-Verlag GmbH Germany, part of Springer Nature
About this chapter
Cite this chapter
Ye, L. et al. (2020). Using CNN and Channel Attention Mechanism to Identify Driver’s Distracted Behavior. In: Pan, Z., Cheok, A., Müller, W., Zhang, M. (eds) Transactions on Edutainment XVI. Lecture Notes in Computer Science(), vol 11782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61510-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-662-61510-2_17
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-61509-6
Online ISBN: 978-3-662-61510-2
eBook Packages: Computer ScienceComputer Science (R0)