Abstract
Traffic safety is a problem that concerns the worldwide. Many traffic accidents occur. There are many situations that cause these accidents. However, when we look at the relevant statistics, it is seen that the traffic accident is caused by the behavior of the driver. Drivers who exhibit careless behavior, cause an accident. Preliminary detection of such actions may prevent the accident. In this study, it is possible to recognize the behavior of the state farm distracted driver detection data, which includes nine situations and one normal state image, which may cause an accident. The images are preprocessed with the LOG (Laplasian of Gaussian) filter. The feature extraction process is carried out with googlenet, which is the convolutional neural network architecture. As a result, the classification process resulted in 97.7% accuracy.
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
Similar content being viewed by others
References
Liu, T., Fang, S., Zhao, Y., Wang, P., Zhang, J.: Implementation of training convolutional neural networks. arXiv preprint arXiv:1506.01195 (2015)
Škrjanc, I., Andonovski, G., Ledezma, A., Sipele, O., Iglesias, J.A., Sanchis, A.: Evolving cloud-based system for the recognition of drivers’ actions. Expert Syst. Appl. 99, 231–238 (2018)
Wang, X., Liu, Y., Wang, F., Wang, J., Liu, L., Wang, J.: Feature extraction and dynamic identification of drivers’ emotions. Transp. Res. Part F Traffic Psychol. Behav. 62, 175–191 (2019)
Olabiyi, O., Martinson, E., Chintalapudi, V., Guo, R.: Driver action prediction using deep (bidirectional) recurrent neural network. arXiv preprint arXiv:1706.02257 (2017)
Braunagel, C., Kasneci, E., Stolzmann, W., Rosenstiel, W.: Driver-activity recognition in the context of conditionally autonomous driving. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 1652–1657. IEEE (2015)
Yan, S., Teng, Y., Smith, J.S., Zhang, B.: Driver behavior recognition based on deep convolutional neural networks. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 636–641. IEEE (2016)
Kong, H., Akakin, H.C., Sarma, S.E.: A generalized Laplacian of Gaussian filter for blob detection and its applications. IEEE Trans. Cybern. 43(6), 1719–1733 (2013)
Kong, H., Sarma, S.E., Tang, F.: Generalizing Laplacian of Gaussian filters for vanishing-point detection. IEEE Trans. Intell. Transp. Syst. 14(1), 408–418 (2013)
Ma, W., Lu, J.: An equivalence of fully connected layer and convolutional layer. arXiv preprint arXiv:1712.01252 (2017)
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732 (2014)
Cengil, E., Çınar, A.: A new approach for image classification: convolutional neural network. Eur. J. Tech. 6(2) (2016)
Accelerate Machine Learning with the cuDNN Deep Neural Network Library. https://devblogs.nvidia.com/accelerate-machine-learning-cudnn-deep-neural-network-library/
Han, S., Meng, Z., O’Reilly, J., Cai, J., Wang, X., Tong, Y.: Optimizing filter size in convolutional neural networks for facial action unit recognition. arXiv preprint arXiv:1707.08630, 26 July 2017
Agostinelli, F., Hoffman, M., Sadowski, P., Baldi, P.: Learning activation functions to improve deep neural networks. arXiv preprint arXiv:1412.6830 (2014)
Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1–6. IEEE, August 2017
Lee, C.Y., Gallagher, P.W., Tu, Z.: Generalizing pooling functions in convolutional neural networks: mixed, gated, and tree, In: Artificial Intelligence and Statistics, pp. 464–472 (2016)
An Intuitive Explanation of Convolutional Neural Networks. https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
Uçar, M., Uçar, E.: Computer-aided detection of lung nodules in chest X-rays using deep convolutional neural networks. Sakarya Univ. J. Comput. Inf. Sci. 2(1), 41–52 (2019)
LeCun, Y.: LeNet-5, convolutional neural networks (2015). http://yannlecun.com/exdb/lenet
Abouelnaga, Y., Eraqi, H.M., Moustafa, M.N.: Real-time distracted driver posture classification. arXiv preprint arXiv:1706.09498 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cengil, E., Cinar, A. (2019). Classification of Human Driving Behaviour Images Using Convolutional Neural Network Architecture. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-29859-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29858-6
Online ISBN: 978-3-030-29859-3
eBook Packages: Computer ScienceComputer Science (R0)